Two Hat

Two Hat AI

Two Hat AI

Two Hat AI

Two Hat AI

Two Hat AI

Two Hat AI

Two Hat AI

Two Hat AI

Two Hat AI

Two Hat AI

I've built an open-source USB-C debug board around the ESP32-S3 that lets AI control real hardware through MCP

I've been building a hardware debugging tool that started as "A one board to replace the pile of instruments on my desk" and evolved into "A nice all in one debugger / power supply" and finally with the advent of Claude Code and Codex "an LLM could just drive the whole thing." With the nice help of Claude, the UI and Firmware became more powerful than ever. BugBuster is a USB-C board with: AD74416H — 4 channels of software-configurable I/O (24-bit ADC, 16-bit DAC, current source, RTD, digital) 4x ADGS2414D — 32-switch MUX matrix for signal routing DS4424 IDAC — tunes two DCDC converters (3-15V adjustable) HUSB238 — USB PD sink, negotiates 5-20V 4x TPS1641 e-fuses — per-port overcurrent protection Optional RP2040 HAT — logic analyzer (PIO capture up to 125MHz, RLE compression, hardware triggers) + CMSIS-DAP v2 SWD probe The interesting part is the software stack. Beyond the desktop app and Python library, there's an MCP server that exposes 28 tools to AI assistants. You connect the board to a circuit, point your token hungry friend at it, and describe your problem. The AI can configures the right input modes (with boundaries), takes measurements, checks for faults, and works through the diagnosis and debugging autonomously. It sounds gimmicky but it's genuinely useful. Instead of being the AI's hands ("measure this pin", "ok now that one", "measure the voltage on..."), you just say "the 3.3V rail is low, figure out why" and it sweeps through the channels, checks the supply chain, reads e-fuse status, and comes back with a root cause. The safety model prevents it from doing anything destructive, locked VLOGIC, current limits, voltage confirmation gates, automatic fault checks after every output operation. It allows for unattended development / testing even with multiple remote users. It can read and write to GPIOs, decode protocols, inject UART commands end much more. Full stack is open source ESP-IDF firmware (FreeRTOS, custom binary protocol, WiFi AP+STA, OTA) RP2040 firmware (debugprobe fork + logic analyzer + power management) Tauri v2 desktop app (Rust + Leptos WASM) Python library + MCP server Altium schematics and PCB layout GitHub: https://github.com/lollokara/BugBuster submitted by /u/lollokara [link] [comments]

Having my cake...

After seeing what folks were doing with OpenClaw, I was salivating, but I was also fearful, so I did nothing. I'm one of those who have lots more fear than greed. I also saw Claude Code's (CC) "dangerously skip permissions" but again was too scared to try it. At the same time, having to approve countless permissions was driving me up a wall. So I put my thinking hat on and came up with a workaround. I had an older laptop lying around (i5, 8GB RAM). After charging that long dead battery, I made a backup of all files that may or may not have been important, copied down all keys, and then did a full wipe and install with Ubuntu. For my purposes, I chose to call this laptop: Boom From my regular computer, I had CC walk me through setting everything up. Here's the steps involved: Wiped the Yoga (old laptop), installed Ubuntu SSH key auth from my main machine (Monster) to the Yoga (renamed "Boom") Passwordless sudo on Boom Node.js + Claude Code CLI Disabled sleep/suspend/lid-close — it runs headless with the lid shut Wide-open `settings.json` — every tool auto-approved I talk to CC on my main machine. When there's a build task, CC on Monster: - rsyncs the source files to Boom - SSHs in and launches CC on Boom with `--dangerously-skip-permissions` - Boom builds autonomously — no permission prompts, no babysitting - Claude on Monster pulls back the results for my review - Deletes the working files on Boom Boom never has access to my main file system, credentials, or sync infrastructure. It only ever gets disposable copies of what it needs for the current job. Total cost: $0 and an afternoon of fighting Windows before wiping it (the hardest part was getting Windows to let go) Any machine that runs Ubuntu and Node.js works. I only have to approve one permission. Giving my Monster's CC access to Boom. When it gets on Boom, it tasks CC there to do everything. If Boom gets trashed, I lose maybe a half hour doing a full wipe and reinstall Ubuntu. I had to go to a meeting. I gave Monster's CC a task to work on a website, approved the permission, came back two hours later and the job was done. Peace of mind. Big grin! submitted by /u/ButterflyEconomist [link] [comments]

AWF CLI – A deterministic CLI workflow engine in Go for AI & Dev tools

Hello sub, First of all, I should mention that English is not my native language. While I wrote this post myself, I used Gemini to help refine the grammar and phrasing. For the past few months, I’ve been working on a side project called "AWF" (AI Workflow Framework). The code for the main CLI tool is now available on GitHub for everyone (including a dedicated Claude skill). I built this tool because I wear two hats: I’m a tech enthusiast, but I also run a company. Managing multiple Claude sessions with various agents simply doesn't scale with my daily responsibilities. I wanted a way to test my ideas and improve my workflow without sacrificing my standards — I’m a firm believer in TDD, QA, and high engineering standards. On the AI side: Efficiency: I don’t want to waste tokens. If I ask an agent to perform TDD, I want real TDD. Determinism: I want to call my CLI tools because they are predictable. Flexibility: I want to use Claude, Gemini, or any other model without arbitrary limitations. I don't want to "cross my fingers" hoping an agent calls the right tool. That’s why I built AWF in Go. It’s a workflow/state machine engine designed to orchestrate CLI tools (including LLMs like Claude, Gemini, and Codex). AWF isn’t another "magic" AI wrapper. It’s a professional tool for those who know how to manage their context window and want to build deterministic, industrial-grade workflows using CLI outputs (stdout). How it works: With AWF, you design your workflow through discrete steps. Each step can run a CLI program, a shell script, or an AI agent. You can pass parameters as options, capture stdout for the next step, or abort the workflow on stderr. It supports transitions, pre/post-events, loops, and retries. The better you know your CLI basics, the more freedom you have to build complex systems. The "AI Step": There is a specialized step for AI interactions. You provide a prompt — which supports variables via Go templates — and AWF executes it according to your design. Want to fail fast? No problem. Continue on error? Easy. Call a nested workflow? That’s actually how I update the AWF skill on every new PR. AWF supports the XDG base directory specification, and workflows can be defined locally or globally with override support. It’s already working great for my own projects, but I want to keep improving it. I’d love to get feedback, especially since AWF is now built using its own workflows! If you’re a Go developer, I’d highly appreciate your insights. Check out the tests and the AWF organization on GitHub; there are plenty of examples to help you get started (the skill will help a lot). My implement workflow Overview of my workflows I’ll be happy to answer any questions. I’m based in France, so please keep the time zone difference in mind. Have a nice day! submitted by /u/pockystarfr [link] [comments]

I had zero experience with Raspberry Pi and built a working proof of concept in a weekend with Claude's help

I've always wanted to mess around with Raspberry Pi but never knew where to start. I have a habit of starting projects and not finishing them so I figured it would just be another expensive box on a shelf. I asked Claude what I'd need to build a vehicle-mounted camera system that automatically shoots and GPS geotags photos while driving — basically a DIY street view rig. It gave me a specific hardware list, where to buy everything, and what it would cost: Raspberry Pi 5 8GB Waveshare 8-channel relay HAT Kiwifotos shutter release cables Emlid Reach M2 GPS receiver Sony a5100 with a TTArtisan 7.5mm fisheye lens I'm literally starting from zero, never done anything like this with building my own hardware. Claude walked me through every step as hardware arrived — flashing the OS, SSH setup, configuring VNC, debugging GPS issues. Two days later I have a live web dashboard on my phone showing real GPS coordinates, storage, camera status and session controls updating every second. I'm still waiting on the camera and power supply to test actual shutter triggering, but I'm further along than I've ever gotten on a project like this, faster than I expected, starting from nothing. Claude has been game changing for me. I'm a hobbyist doing this in my spare time and this is a very complex project — custom hardware, embedded Linux, GPS parsing, relay control, a web interface. I'm literally inventing solutions with its help. It's like having a PhD genius on call who you can ask anything, any time. I think this is just the start of what's possible with it. submitted by /u/pacsandsacs [link] [comments]

Claude' Cycles

Yesterday, one of the greatest figures in computer science, Donald Knuth, published a scientific note on the website of Stanford University. It’s hard to call it a full “study” because it was literally published only two days after submission, but research notes follow a different process. In the note, Knuth explains how the latest AI model, Claude Opus 4.6, managed to solve a complex mathematical problem he had been working on for weeks without reaching a final solution. The problem involved decoding certain graphical structures related to directed Hamiltonian cycles in three dimensions, something closely connected to his famous book The Art of Computer Programming. To imagine the problem simply, picture a large cube made up of many points (like a very large Rubik’s cube). The task was to move between these points according to specific rules. The goal was to construct three separate paths, where each path must: pass through every point in the cube exactly once return to its starting point Mathematicians call this a Hamiltonian cycle. Movement along these paths is directed, meaning it’s like one-way streets: you have strict rules about which direction you can move, and you cannot go backward. The real challenge was that the connections between the points had to be split into three completely separate paths, with no edge shared between them. Knuth mentioned this problem in a draft of The Art of Computer Programming, where it appeared as an open problem — meaning there was no known general solution for all sizes. The breakthrough came when the AI managed to invent rules that allow these paths to be constructed. What happened next is interesting. Knuth’s friend Philip Staprs decided to ask Claude about the problem. The AI began the way many of us have seen before: it wrote a structured plan, broke the problem into steps, and started working through them. The model began writing Python code, experimenting with deep search strategies. Naturally, you might expect it to fail and it did fail at first. But one feature of modern models is that they can re-examine their approach when they encounter errors. The model kept refining its pattern analysis until it eventually reached a solution. In the end, Claude produced Python code that solves the problem for all odd values, and Knuth himself confirmed that the solution is mathematically correct and remarkably creative in its problem-solving approach. That assessment comes directly from Knuth. So who is Donald Knuth? Donald Knuth is a renowned American computer scientist and mathematician, Professor Emeritus at Stanford University, often called the “father of algorithm analysis.” He is best known for authoring the multi-volume series The Art of Computer Programming. In 1974, he received the Turing Award, which is often considered the unofficial Nobel Prize of computer science. Knuth said that after seeing this progress in automated reasoning, he may need to reconsider his views on generative AI. The solution currently works for odd sizes, while the even cases remain unresolved, but what happened here is still a major step forward. What makes this particularly notable is that this assessment is coming from a scientist who has no stake in the AI industry not someone from Google or Anthropic, not someone invested in AI companies. From his own words, he had previously been somewhat skeptical about the productivity of large language models. I’ll leave you with a quote from Knuth at the end of his note: “All in all, however, this was definitely an impressive success story. I think Claude Shannon’s spirit is probably proud to know that his name is now being associated with such advances. Hats off to Claude!” submitted by /u/Fun-Necessary1572 [link] [comments]