I was tired of watching LLM agents fail at basic Git operations. Standard integrations pass raw text, hang on pagers, or scream because they can't parse unstructured ⁠git diff⁠ outputs. git-courer is a full Model Context Protocol (MCP) server written in Go that treats Git properly. No bash spawning, no unstructured text to parse. Everything communicates via structured JSON. Here is an actual commit message it generated completely locally: fix: fix mcp server connection handling WHY The previous implementation lacked proper error handling for connection failures in the MCP server, leading to unhandled panics or silent failures when the local LLM backend was unreachable. WHAT * Added connection timeout logic to the local client calls. * Implemented retry mechanisms with exponential backoff for transient backend errors. The Architecture & Tool Pack Read Tools (status, diff, history, blame): Completely structured JSON and fully paginated. A single ⁠status⁠ call replaces over 5 standard Git commands for the agent. Write Tools (commit, merge, rebase, branch, stash, stage, sync...): Every single mutation auto-creates a backup before executing. If the LLM messes up, a ⁠RESTORE⁠ command brings you back exactly where you were. Safety Model: Destructive operations (hard resets, force pushes, branch deletions) require an explicit ⁠confirmed=true⁠ gate. The agent is forced to ask you first. ⁠dry_run=true⁠ is also available for peace of mind. The Semantic Annotator (Why it's different) Instead of just feeding raw code to the LLM, git-courer uses ⁠go-enry⁠ + ⁠go-tree-sitter⁠ to parse the AST and tag every hunk semantically before the LLM even sees it. It detects tags like ⁠NEW_FUNC⁠, ⁠MOD_SIG⁠, ⁠MOD_BODY⁠, ⁠DELETED⁠, and ⁠BREAKING_CHANGE⁠. The commit type (⁠feat⁠, ⁠fix⁠, ⁠refactor⁠) is determined deterministically from these AST tags rather than guessed by the model. The Commit Pipeline Atomic Commits: One staged area = one commit. It actively prevents the agent from creating gian

reddit.com/settings/data-request https://gamma.app/docs/Reddit-Brain-qt0g7e5vktlgifm Implementation Blueprint Your questions answered. Three steps to go from zero to a fully operational Reddit Brain. Step 0: Download Your Archive Go to reddit.com/settings/data-request and request your full data export. You'll receive a ZIP file containing comments.csv and posts.csv — everything you've ever posted on Reddit. Step 1: Get the Data Action: Request your export at reddit.com/settings/data-request . Then: Download ZIP, extract comments.csv and posts.csv . Optionally run reddit-user-to-sqlite to build a parallel SQLite archive for richer querying. Step 2: Build the Brain Action: Load into Sheets or a database. Clean, tag, and compute word count and engagement metrics. Then: Add LLM passes for canonical_question , topic, tone, and content type. Push into a vector store; connect via n8n or your preferred orchestrator. Step 3: Exploit the Hell Out of It Action: Generate content backlogs, podcast outlines, FAQs, scripts, and social copy from your corpus. Then: Use agents to draft from your own history, keep messaging on-brand, and refresh the archive with new exports on a schedule. submitted by /u/jdawgindahouse1974 [link] [留言]

I have been using PandaCSS + Ark UI to build out my components on my app. I kind of like it, I mean really its an alternative to Tailwind basically using CSS in JS, but with full types so you don't need an extension to get auto-complete. Where it gets clunky for me though is the build tooling having to build the styled-system directory with everything...but I mean I guess Tailwind has its own building configuration. I'm just curious cause I've kind of liked it, but really see it used much. Most I can find on info is like videos and stuff when it first came out. submitted by /u/k032 [link] [留言]

I built a terminal app that paces slow breathing at 6 breaths per minute for vagal tone training. It's a single Python file, stdlib only, no dependencies — just run breathe and follow the bar. I'm a cardiology patient (HFrEF). Slow breathing at resonance frequency is one of the few non-pharmacological interventions shown to improve cardiac vagal tone and baroreflex sensitivity (Bernardi et al., Circulation 2002; Lancet 1998). I wanted a frictionless daily habit tool — no app store, no account, n

I started off thinking I needed to simply memorize Modules and go through each and find a way to apply each of them linearly through the docs. Thinking about it that was kind of a retarded approach, but i blame it on classroom conditioning. My inconsistent life stability as well, going off and on with studying. Anyway, I found progress in freeCodeCamp and I've took these notes: Project Completion List: Report card printer | Employee profile generator | Bill Splitter Movie ticket calculator | Weather Travel Planner | Apply Discount Function Caesar Cipher | RPG Character Creator | Pin Extractor | Number Pattern Generator Medical Data Validator | In Python, code blocks are determined by indentation. Redundancy Is A BIG Logic Problem I have in learning Python. Think of parameters as placeholder variables that act as "slots" for the values you pass into functions when you call them. To use the parameters, you have to pass in "arguments". Arguments are the values you pass to a function when you call it. In Python it seems that you should learn to do things with incredible detail and specifics. In the FCC it seems an unspoken lesson is to pay careful attention to what is not said, don't under-cut and don't exceed the expectation. That might be something to ponder later. In note taking i have also found that it is much more meaningful and efficient to only record pointers, not executions or further examples. Logic Thinking and Problem Solving are both things that have to be developed by you. They cant be memorized or given to you. You should give yourself obvious recognition to look back on, It helps you remind yourself when you think your not making progress. order matters whenever it comes to for loops Also, an if statement is NOT a for loop Memorizing isn't learning, It's memorizing. submitted by /u/Big_Example_3390 [link] [留言]