GitHub Copilot

GitHub Copilot AI

GitHub Copilot AI

GitHub Copilot AI

GitHub Copilot AI

GitHub Copilot AI

GitHub Copilot AI

GitHub Copilot AI

GitHub Copilot AI

GitHub Copilot AI

GitHub Copilot AI

Open-sourced an MCP server that catches the security mistakes Claude / Cursor / Copilot actually make

AI coding tools like Claude, Cursor, and Copilot sometimes write code that looks fine but quietly leaves your app wide open like turning off security checks to make an error go away, or telling you to install a software package that doesn't actually exist (which means a bad actor can create that name later and take over anything that installs it). Made a free tool that scans your project or any GitHub repo and tells you what's broken, ranked by how bad, with the exact commands to fix it. https://github.com/ExecutiveKoder/sureguard-code-scanner submitted by /u/sks8100 [link] [comments]

Switched from Copilot to Claude and it's painfully slow. How do I use it better?

Hey everyone, I recently moved over from GitHub Copilot to Claude because everyone keeps hyping up how good Opus 4.7 is for advanced software engineering. In Copilot, I used Opus 4.7 and it felt snappy, fast, and great. But using Claude directly (via the desktop app), it feels very, very slow. It takes ages on basic tasks and burns through incredibly long sessions for things that should be relatively simple. Right now, I have my settings on "Max Effort" by default because I wanted the highest capability, but it's just overthinking everything. Honestly, I don’t know what to manually choose for each prompt, and I don't want to keep micromanaging the settings. Ideally, I just want an auto-mode that automatically chooses the right effort level depending on the complexity of the task, low effort for basic things and high effort only when it's actually needed, so the sessions are more effective and fast, just like how it felt back in Copilot. A few questions for the power users here: Is there a way to enable an automatic/adaptive effort mode in the app? How do I make it scale its thinking time automatically based on what I'm asking? Does Claude Code handle this better than the Desktop app? I'm thinking of switching to the CLI tool, but does it have a true "auto" effort mode that stops it from lagging on easy tasks? Any advice on how to optimize this setup so it's at least as fast as Copilot would be heavily appreciated. Thanks! submitted by /u/Feisty_Leather5848 [link] [comments]

I built a persistent memory layer for Claude Code, Codex, Cursor, and other coding agents

Claude Code gets much better when you give it project context. CLAUDE.md helps. Skills help. Session summaries help. But I kept running into the same problem: The memory was tied to one tool, one session, or one folder structure. Once I started using multiple agents across the same codebase, the system became messy. Claude knew one thing. Cursor knew another. Codex started from zero. Important decisions lived in old chat logs. Debugging context disappeared after compaction. So I built AgentMemory. The idea is simple: Instead of treating memory as chat history, treat it like project infrastructure. A coding agent should be able to read: - what this repo does - what decisions were already made - what files matter - what bugs were already investigated - what patterns the project follows - what should not be repeated - what context is stale And this memory should work across agents, not just one Claude session. The main difference from a normal CLAUDE.md setup: CLAUDE.md is a great entry point. AgentMemory is trying to be the shared memory layer behind the agents. So Claude Code, Codex, Cursor, Copilot, or any agent can use the same project memory instead of rebuilding context every time. I also wanted the memory to be benchmarkable. Because “the agent remembers things” is not enough. The useful questions are: - did memory improve the task? - did stale memory hurt the result? - did the agent retrieve the right context? - did it avoid repeating old mistakes? - can another agent use the same context? Still early, but the repo is here if anyone wants to try it or give feedback: https://github.com/rohitg00/agentmemory Curious how others here are handling memory across long-running Claude Code projects. submitted by /u/SeveralSeat2176 [link] [comments]

GitHub Copilot user thinking of switching to Claude, is Pro ($20) enough for Android dev?

Hey everyone, I’m currently using GitHub Copilot for Android development, but I’m thinking about moving to Claude because I keep hearing good things about it for coding. Most of my work is: Android app development Kotlin / Jetpack Compose Refactoring and debugging Long coding sessions with lots of context I’m trying to understand which Claude plan is actually enough for a solo developer. For people using Claude heavily for coding: Is the $20 Pro plan enough for daily Android development? How fast do you usually hit the limits? If I hit the Pro limit, can I instantly upgrade to Max and continue working immediately? Would love to hear real experiences before switching fully from Copilot. submitted by /u/Feisty_Leather5848 [link] [comments]

Built a tool that turns websites into structured design docs for AI workflows

Been experimenting with a tool that converts websites/screenshots into structured design documentation. The original problem was that screenshots alone weren’t enough for reliable UI understanding inside AI/browser-agent workflows. So the tool tries to combine: visual hierarchy DOM/CSS structure spacing systems typography patterns interaction behavior reusable component analysis The interesting part is seeing how different products structure their UI systems internally. Still early and improving daily, but curious what people here think would make something like this genuinely useful in AI/dev workflows. Happy to Share Link -- submitted by /u/hiehie [link] [comments]

Experimenting with screenshot + DOM analysis for better UI understanding

Been experimenting with a tool that converts websites/screenshots into structured design documentation. The original problem was that screenshots alone weren’t enough for reliable UI understanding inside AI/browser-agent workflows. So the tool tries to combine: visual hierarchy DOM/CSS structure spacing systems typography patterns interaction behavior reusable component analysis The interesting part is seeing how different products structure their UI systems internally. Still early and improving daily, but curious what people here think would make something like this genuinely useful in AI/dev workflows. submitted by /u/hiehie [link] [comments]

I tracked every dollar I spent on AI coding tools for 60 days and math is uglier than I thought but probably not in the way you'd guess.

Well so I kept telling myself my AI tool spend was fine the way you tell yourself your subscription bloat is fine. vibes-based finance. decided to actually track it. 60 days. every dollar, every tool, every minute I could log honestly. did it for myself, but the numbers are interesting enough I figured I'd share. context: solo dev / freelancer doing mostly web work… react, node, some python. small/mid tier clients. I bill hourly, which means time saved is direct revenue, which is the only reason I'm able to be honest about ROI here. subscriptions I have: cursor pro: $20/mo claude pro + claude code api usage: $110/mo (api was the variable, plus alone is $20) chatgpt plus: $20/mo (mostly inertia at this point, honestly) github copilot: $10/mo coderabbit: $15/mo v0 + occasional one-offs: $25/mo across two months total subscription spend: roughly $200/mo, $400 over period. this is the number people argue about on twitter/X. it is also, I now realize, least interesting number in entire calculation. here’s where it gets interesting: I tracked time spent on three categories: time generating output that ended up in prod: clear win, easy to count, 62 hours over 60 days. at my rate that's a real number time fixing AI output that was wrong but plausible: this is where it got bad. 28 hours. almost half as much time as productive work time switching between tools, debugging specific weirdness and arguing with an agent that was wrong: 14 hours so for every productive hour of AI use, I was burning roughly 40 minutes of overhead. nobody talks about that 40 minutes and depending on the kind of work, it was worse and refactoring legacy code was almost 1:1 productive vs wasted time. this is how I actually saved: I tried to estimate what same work would've taken without AI tools. best estimate: 62 productive hours would've been 110-130 hours without AI assistance. so net savings of 50-70 hours over 60 days. at my hourly rate that pays for the subscriptions many times over. so verdict is yes worth it. but the verdict everyone wants to hear (AI made me 3x faster) is wrong. it's more like 1.7-2x on a generous and that's only after subtracting 42 hours of overhead. line items I'd cut and keep: going through receipts, here's what surprised me: kept: cursor pro, claude code, coderabbit on watch: chatgpt plus (using it less and less, it's basically a habit) cut: copilot (overlaps too much with cursor for my workflow), v0 (only useful for specific work) the surprise was coderabbit, honestly. cheapest line item on my list and one I was most ready to cut going in but when I went back through 60 days of pull requests, the time I would've spent doing my own line by line review of agent output, which I now do religiously after a few burns was massive. an automated first pass cost me $15 and saved probably 6-8 hours of review work over the period. that's highest ROI per dollar of anything on the list, and I almost didn't track it because it felt too small to matter. generation tools are sexier. review tools punch way above their weight when you're using generation tools heavily. that's the actual finding. takeaway nobody put in their twitter thread: most of the cost of AI tools conversation is about the wrong number. subscription cost is rounding error compared to time cost of bad output and the way you minimize that time cost isn't by buying a better generation tool, it's by buying a verification tool to sit on top of whatever you're already using. if I had to start over, I'd buy the cheapest decent generation tool I could find and put my money on the review/verification layer instead that's the inversion of what the marketing tells you to do. tl;dr: tracked AI tool spend for 60 days. subscriptions ($200/mo) were the easy and least interesting number. - real cost was 42 hours of overhead per 60 days of productive use. - real savings were 50-70 hours, which is worth it but it's 1.7-2x not 10x. - biggest surprise was that cheapest tool on my list had highest ROI/ dollar by margin. what's your actual stack costing you, including the time tax? I'm curious if other people who've tracked this seriously are seeing similar overhead numbers or if I'm just bad at this. submitted by /u/thewritingwallah [link] [comments]

Important workflow question: How do I set up an agent safely to not have to constantly review and monitor every cmd command it runs?

Basically, I have been vibe coding an app for over a year now. I have seen many devastating examples of coding agents deleting crucial files - especially when it applies to files outside the current repo - and I am therefore very unconfortable to grant complete access to the copilot agent. As such, i have very few of the agent's request on Auto-approve, so I have to manually click approve on nearly all messages. However, I have seen compelling evidence at this point that coding agents are able to iterate on their own for long periods of time, and that experienced developers set up a configuration that ensures both that: (1) The AI is confined into a limited environment; both in terms of the code base itself and the external stuff like git etc. (2) Because the ai agent is safely confined, all messages can be set to auto-approve, so you don't have to manually read every message. So does anyone have a recommended setup for how this is done? Ideally some sort of blog or tutorial video that shows how to set it up i, e.g Claude Code or Github Copilot. Thank you :) submitted by /u/NowIsAllThatMatters [link] [comments]

Claude Api Cost TOOO much, 10$ in single edit!!

I’ve been using GitHub Copilot for my coding task regularly, the Sonnet or GPT model usually costs me about one premium request per request, that translate to 0.04$. Out of curiosity, I decided to compare this with direct API costs. I signed up and added $20 to try Claude Code with the Sonnet 4.6 (High) model on a similar task. It went through the planning phase and moved into edit mode, but when I checked my console afterward, I was surprised to see it had used $10 for that single task about ~16M tokens in and ~90K out! It feels like this might be a bit much for individual, and I hadn't really heard any warnings about it, infact people keep saying about its cost effective. Even for a complex task, Copilot would have only cost around $0.3 for a handful of requests. I’m wondering if I might have set something up incorrectly, but it was a bit frustrating that the default experience for a new user turned out to be so expensive. Has anyone else had a similar experience? I’d like to know how you guys are managing API costs or if you have any tips, though I am not expecting any magic after what I've seen. Now I am feeling like even trying this sh!t. EDIT: see no one even caring what could have happened or helping me with, just pointing out i used 16M token as periodical reason or some typo mistake in this post, I mean siriously! submitted by /u/frostechgamestudio [link] [comments]

I built Skills Curator - a context-aware Claude skill manager that understands your stack

https://preview.redd.it/r1leyp4g2x0h1.png?width=1078&format=png&auto=webp&s=adca2d7a39b77c859665d5281818b84010bb501f Repo: https://github.com/captkernel/Skills_Curator Install: npx skills add captkernel/Skills_Curator Huge catalog, no memory of past decisions, re-evaluating the same skills every few weeks. That was my loop. But the deeper issue: every skill you install wholesale brings its author's opinions and tradeoffs into your codebase. Stack enough of them and your project stops being yours. So I built Skills Curator — a Claude Code skill whose entire pitch is judgment, not plumbing. What makes it different from npx skills, asm, or vercel/find-skills: 1. Skill customization. Don't just install — decompose. Strip any skill to its most granular parts, understand what each piece does, rebuild a version for your stack and constraints. Skills Curator structures that process. One voice shaping the output. Yours. 2. Comes to you. Reads your config files (deps, CLAUDE.md) at session start. Nothing changed, nothing happens. Added a framework? It surfaces top picks as a quiet observation — not a pitch. 3. Symptom-based matching. "Tests are slow", "deploys are manual", "UI looks ugly" — maps your complaint to skill categories via a 17-pattern table. 4. Pre-install security scan. 14 risk patterns: RCE, hardcoded API keys, GitHub PATs, base64 obfuscation, credential-store access. Automatic before any verdict. 5. Decisions persist forever. Every evaluation stores pros/cons/conflicts/verdict/partial-adoption plan. Same skill resurfaces six months later — you read your past judgment in 5 seconds. --export-eval for PR-ready markdown. 6. Ranks by fit, not popularity. Tag overlap × trust tier. A 200-install skill that matches your stack beats a 50,000-install one that doesn't. 7. Cross-agent portability. 55 platforms — Claude Code, Copilot, Cursor, Codex, Gemini CLI, Cline, Windsurf, OpenCode, and 47 more. 8. Two tiers, same plugin. Lite is default — pure markdown, zero friction. Python tier (~2.3k LOC, stdlib-only) for large catalogs and cross-device Gist sync. Different registry paths, no conflicts. 37 pytest cases, CI on 3 OS × 4 Python versions, MIT licensed. Genuinely curious about the activation trigger problem — when should an agent proactively suggest skills vs. stay silent? If you've thought about this, I'd love to hear it. submitted by /u/captkernel99 [link] [comments]

AgentKanban for VS Code - A task board with AI agent harness integration. Create and plan tasks with real-time collaboration, then hand off to GitHub Copilot

Hi everyone. I wanted to introduce a tool / product that I've been working on for a while. It's a web application and VS Code extension for use with Github CoPilot (I'm planning to develop integration for other agent harnesses soon). The web app and remote boards are at: https://www.agentkanban.io The VS Code extension is at VS Code Marketplace (https://marketplace.visualstudio.com/items?itemName=appsoftwareltd.agent-kanban-vscode) or the Open VSX Registry (https://open-vsx.org/extension/appsoftwareltd/agent-kanban-vscode). The TLDR It's a collaborative Kanban board / task management app which supports hand off to Github CoPilot in VS Code, and captures the ongoing user / agent conversation context on the task for resumption in new chats (with context curation tools). The context collection ignores tool use to prevent bloat in the captured context. AgentKanban also has features for improving agentic coding session quality such as an optional plan / todo / implement workflow and support for Git worktree creation and clean up for working on concurrent tasks. The tool is an evolution of an earlier VS Code kanban extension (https://marketplace.visualstudio.com/items?itemName=AppSoftwareLtd.vscode-agent-kanban) I built which proved fairly popular but only catered for a local file based workflow. The new version with the remote board improves the reliability of context capture, with lots of developer experience improvements. It's a tool that I use everyday in my own agentic coding workflows, and I can honestly say that it improves the quality of the code produced and reduces friction in organising working on concurrent features. I hope you find it useful and would really appreciate your feedback on how you use it, what you think it does well, or any improvements you think could be added. Many thanks for your time reading this 🙏 https://preview.redd.it/tkujgmm93w0h1.png?width=1597&format=png&auto=webp&s=0a2d2bb41f787b538ca9ded9d00946c731eadbc9 submitted by /u/gbro3n [link] [comments]

We built a free tool that generates a DESIGN.md from any live URL, keeps AI coding agents on-brand

The Google Labs DESIGN.md spec launched last month, it's a machine-readable markdown file your AI coding agent reads to understand your design system. This tool automates creating it. Paste any public URL: the tool extracts CSS variables, typography, Tailwind classes, and component patterns, then an AI assembles them into a spec-compliant DESIGN.md. Visual editor lets you fine-tune tokens before you download. Drop the file in your repo root and your agent has a consistent design reference across every session. Works with Cursor, Claude Code, GitHub Copilot, Aider, and Continue. Free, no signup. https://www.masumi.network/tools/design-md https://reddit.com/link/1tb2tki/video/tlqzrvm1sp0h1/player submitted by /u/thinkgrowcrypto [link] [comments]

I built an autonomous engineering agent on top of Claude Code. Self-improving routing, cross-session memory, process intelligence, P2P team learning.

Some of you might remember my posts about claude-bootstrap (v3.6 was the last one — cross-agent intelligence). I skipped v4 entirely because v5 shipped days later. What started as an opinionated Claude Code setup has become something fundamentally different. The problem I'm solving: Every AI coding tool today is an amnesiac. When a session ends, everything the agent learned — project conventions, reviewer preferences, codebase idioms — evaporates. The next session starts from scratch. And if you use multiple AI tools across projects, you have zero unified visibility into what's happening. I think the industry is converging on a spectrum: Level 0: Autocomplete (Copilot, TabNine) Level 1: Chat Assistant (ChatGPT, Claude) Level 2: Project-Aware Assistant (Cursor, Continue) Level 3: Task Agent (Devin, Claude Code Agent) Level 4: Autonomous Engineering Platform (Maggy) ← this is what I built The difference at Level 4: multi-model orchestration, self-improvement from every task, process intelligence that learns from CI/reviews/deploys, cross-session memory, and P2P team learning. What Maggy actually does Chat — Session Takeover: Auto-detects all running Claude Code sessions across your projects. Shows session history, prompt counts, duration. You can `--resume` into any session from the dashboard. Right now I have 7 active sessions across 4 projects visible at a glance. Task Triage: Connects to GitHub Issues and Asana. AI-ranks tasks by priority. One-click "Plan" or "Execute" buttons that spawn the right CLI with codebase context pre-injected from an intent code property graph (iCPG). Process Intelligence: This is the part most tools completely ignore. Maggy collects signals from the full SDLC — CI results, PR review comments, CodeRabbit findings, merge patterns, deploy results. It learns which code patterns cause test failures, what reviewers consistently flag, and preemptively fixes issues before they reach reviewers. > "Your reviewer always flags missing error handling in API routes. Maggy added it before the PR was created." That's not prompt engineering. That's autonomous process optimization. Cross-Session Memory (Engram): Maggy identifies 7 distinct amnesia pathologies (anterograde, retrograde, temporal, source, interference, context-binding, confabulation). Engram is a three-tier memory system — local (project-specific), portfolio (cross-project patterns), and mesh (team-shared). Knowledge compounds across sessions instead of evaporating. Maggy Mesh — P2P Team Intelligence: Connects Maggy instances across a team. One developer's CI fix becomes the entire team's knowledge — autonomously. Typed memory classes (scores, patterns, policies, gaps) with provenance and quarantine. A new team member gets the benefit of months of collective learning on day one. Multi-Model Routing: Auto-discovers which CLIs you have (Claude, Codex, Kimi, Ollama) by probing `--help` at startup. Routes by complexity score: Blast 1-3 → ollama (free, local) or kimi (cheap) Blast 4-6 → codex (mid-tier) Blast 7-10 → claude (premium, with validator) Security, tests, docs, architecture always go to Claude regardless. The routing rules are YAML and self-update from task outcomes. 5-Level Self-Improvement: This is the core differentiator. Every task teaches Maggy something: | Level | Frequency | What It Does | |-------|-----------|-------------| | L0 — Real-time | Seconds | Catches tool/test failures, switches models mid-task | | L1 — Task | Minutes | Computes reward score, updates model performance | | L2 — Daily | Hours | Catches CI pass rate drops, disables failing models | | L3 — Weekly | Days | Evolves skill files, adjusts workflow steps | | L4 — Monthly | Weeks | Recalibrates reward signals, tunes the improvement process itself | Budget Tracking: Per-provider token spend with daily limits. When Anthropic hits budget, Maggy routes to OpenAI. When that hits budget, it routes to local Qwen. Work never stops. Competitor Intelligence: RSS + Google News daily briefing for your competitive landscape. The benchmark Built an Expense Tracker (6 tasks) through two pipelines — Maggy (4 models) vs Claude Code alone: | Metric | Maggy | Claude Code | |--------|-------|-------------| | Success rate | 6/6 (100%) | 6/6 (100%) | | Quality score | 7.4/10 | 7.8/10 | | Claude usage | 1/6 tasks (17%) | 6/6 tasks (100%) | | Security issues found | 7 | 0 | Claude alone is faster. But Maggy used it for only 1 out of 6 tasks — 83% reduction in premium compute. And the dedicated security routing caught 7 issues the single-pipeline missed entirely. The question isn't "which tool writes better code today?" — it's "which tool writes better code *next month* than it did *this month*?" Repo: github.com/alinaqi/claude-bootstrap Maggy is built on Claude Code's infrastructure (skills, hooks, MCP). It extends Claude Code with self-improvement, multi-model routing, process intelligence, and team mesh. If you just want the skills/hooks/TDD se

Still lots of goblins

"GPT-5.4 Medium" in github-copilot: I’m ready to edit the code, but first I’m reading the two user-facing docs that mention configuration so I can keep behavior and documentation in sync rather than creating a tiny chaos goblin. submitted by /u/photonenwerk-com [link] [comments]

Migrating from GitHub Copilot Chat… Terminal use?

I’ve just moved from using the GHCP Chat extension in VS Code to using the Claude extension and everything about it is better except for one thing. In GHCP it opened a terminal in vs code that I could interact with. Claude won’t do this and runs its own internal terminal for bash commands, etc. That means I can’t respond to shell prompts or check things directly. Is that just a limitation of how it works or am I missing something? submitted by /u/Broric [link] [comments]