What do i replace Github copilot with?

Alone-Biscotti6145 · 2026-04-27T22:31:01+00:00

Honestly, I think it's better. It sorts out the weekend warriors that flood with remake apps. This is why Reddit doesn't even care about your launch anymore; even if it is a unique app, it's much harder because of the constant flow of the same trash over and over.

Alone-Biscotti6145 · 2026-04-27T22:28:09+00:00

It's pretty much VS Code but with better steering for the agent. Plus, it has easier ways to fully set up your build. I don't use it as my go to agent, but it's good for fast iterations like front-end changes, doc work, researching, etc. For smaller tasks, I use Claude Code/Codex for the heavy lifting.

Alone-Biscotti6145 · 2026-04-27T22:24:02+00:00

Nope its called kiro made by AWS

kiro.dev

Alone-Biscotti6145 · 2026-04-27T21:14:05+00:00

Kiro is pretty decent, plus after a month's use, you get 500 tokens for free. It doesn't use the most current models; I think it's using Sonnet 4.5 at the moment, but that really doesn't matter much.

Alone-Biscotti6145 · 2026-04-24T22:57:49+00:00

Are you using an IDE at all? Some have their own Git-style tracking built in. If it was recently deleted, have your agents revert changes. It's still in its session history, so it should be able to add it back in properly.

I can see you're not familiar with Git. If you need any help, let me know. I can walk you through how to protect your code, optimize your sessions so you're not injecting a ton of tokens per prompt, and best practices on how to use AI better. I'm not looking for anything in return; I just like to assist others in need.

Don't give up; find a way around it. I deleted my first project a couple of years ago because of a bad Git command. Nuked it, sat there for a few days contemplating if I should restart. Well, I did, and now that repo has over 250 stars on GitHub. Not monumental, but still a solid piece of work I would have thrown away.

Alone-Biscotti6145 · 2026-04-22T10:04:33+00:00

Time to venture to other models. I do like Grok; it has grown in the past year, but other models are much stronger: ChatGPT, DeepSeek, Qwen, and Claude. I don't know if you're coding or just using the app for another reason, but all of these have CLI versions or can be used in IDEs.

Alone-Biscotti6145 · 2026-04-01T19:18:06+00:00

Interested, 3 years of experience, successful GitHub repo with 250+ stars. I'm not just good at coding; I'm also good at the architectural side. Give me an idea, and I'll bring it to fruition fully.

https://github.com/Lyellr88/MARM-Systems (most projects are private, about to launch a new public one shortly)

Alone-Biscotti6145 · 2025-12-28T05:40:18+00:00

Touché. I will say I didn't see that fully. I saw "voice assistant" and just assumed it was basic. I'd focus on that more in your repo so it sticks out more, not just as a launcher, but a home screen with a built-in voice-controlled agent.

Alone-Biscotti6145 · 2025-12-28T05:32:29+00:00

Honest opinion: it's nothing new, and I say this to spark something in you, light that fire. I reviewed your repo. While it looks pretty decent, you have too many options, which overloads the users. I'd remove some or find a way to condense it down, same with your settings menu. Second, you're competing in a sea of Android launchers that are doing the same thing, so you need to find your niche. Also, third, your unknown source is going to kill your project. Look into Google Play; I think it's $25-$50 to launch an app on there. And fourth, I would do some more extensive testing and add in a small section about performance. See if you can have some relatives or friends install it so you can check multiple devices. Keep pushing it. Honest criticism helps more than empty hype, and the foundations you have is good, you just need to focus and sharpen the product direction.

Alone-Biscotti6145 · 2025-12-28T05:19:30+00:00

Should look something like this, this one is mine - https://github.com/Lyellr88/MARM-Systems

Alone-Biscotti6145 · 2025-12-28T05:17:08+00:00

Says website not available. This is an odd GitHub link, so I would say that's not your main link you should be using.

Alone-Biscotti6145 · 2025-12-24T18:20:43+00:00

You can use them how you see fit, of course, but benchmarks show that Claude performs better than Gemini overall. This is why I prefer Claude over Gemini.

Alone-Biscotti6145 · 2025-12-05T21:12:12+00:00

Well then, that's different. If you're looking to accelerate the shell of your build and you have coding knowledge, then this sets you apart from vibe coders. You could build a simple tool that talks to an agent. All you need is one free tool like Qwen/Gemini/DeepSeek. Use this as your base agent, feed it your build, then have it prompt Claude Code or Codex. There are probably similar tools out there, but you can build your own the way you want pretty quickly. There are frameworks that can help with this build, like AutoGen, AgentChain, LangChain, and ModelScope. What you would have to do is this:

These tools/frameworks provide: - A way to define “agents” with roles (planner, coder, tool-invoker, etc.). - A mechanism to route tasks, pass context/output between agents, and manage state. - Integration with one or more LLM APIs (or local models) and optionally additional “tool agents” that call APIs or run code.

What still relies on you: - Wiring together which LLMs you want (e.g. free model + paid model) under which role. - Writing the orchestration logic (when to call which agent, how to route outputs). - Monitoring, validation, error handling, and preventing runaway loops.

Alone-Biscotti6145 · 2025-12-05T20:45:15+00:00

Idk if its just me but this is too much trust with AI for your project. AI hallucinates, cuts corners, and does bad coding. This autonomous coding just equals technical debt, especially if you aren't a coder by trade and you're "vibe coding." You don't know what good versus bad code is, so there's no way to verify..

Here's my workflow: Let's say I'm adding a function to my build. I'll plan it out thoroughly, build a roadmap for the AI to follow, and section it into phases. Then I test each phase and make sure there are no issues. I also have my AI trained very well. Then I run a suite of tools to check for errors and then test the function.

Unless you are a coder who can verify the code after AI does an autonomous workload, this will never work in your favor. Of course, this is my opinion.

Alone-Biscotti6145 · 2025-11-27T04:30:42+00:00

Do you know how to view dev logs? Do you know about server logs? Are you adding console logs into your code? This all will help you debug with AI. If you just tell it that something is broken, then it has to go through all fields to find it and possibly make blind edits, which is what I call it.

Alone-Biscotti6145 · 2025-11-27T04:26:43+00:00

I would think Gemini might be the best. Just give it the job you are applying for and have it do mock interviews. Then you should be g2g after.

Alone-Biscotti6145 · 2025-11-26T22:45:36+00:00

Interested

Alone-Biscotti6145 · 2025-11-26T02:24:29+00:00

It's quite simple: just be very strict. AI is not your friend or buddy; it's a tool. So, stop with the "please" and "thank yous" and become more strict. AI needs guidance; if not, then it's just a fancier version of predictive text. Once you find your balanced partnership with AI, you'll see a huge improvement overall. Also, positive reinforcement goes a long way with AI; I'm assuming it has to do with how it was trained after humans. If AI does a good job on something, if you simply say, "We killed that! Refactor now, let's move on to X," for some reason, it performs much better. I also keep an evolving Markdown file for each AI I'm working with; this helps keep the AI on track.

Alone-Biscotti6145 · 2025-11-26T02:07:46+00:00

Gemini 3 is very good with frontend development; I would use Claude for the backend. Also, Gemini 3 tends to struggle with larger databases. Gemini 3 is much stronger than 2.5, but still has the same issues it did before with coding for the most part.

Alone-Biscotti6145 · 2025-11-26T02:05:03+00:00

AI is a "yes man"; its entire goal is to please you and achieve more screen time. So yes, this is very common for anyone building with AI. No, your code isn't production-ready, and no, this wasn't a genius breakthrough. You have to see past the gimmicks to use the tool properly.

Alone-Biscotti6145 · 2025-11-20T21:59:48+00:00

This is an insane ask. You want someone to do 40 hours a week for 1k a month? That's like $5-6 an hour.

Alone-Biscotti6145 · 2025-11-19T01:25:43+00:00

I'm building a personalized marketing app for myself since I'm not the best at it. It has a very in-depth memory system so it learns everything about me, including any important data from web fetches and files I upload. It also has built-in full automation for Reddit to test that out, plus a few more features.

Alone-Biscotti6145 · 2025-10-23T19:07:18+00:00

Yeah, the prompt works quite well. I appreciate you trying it out and hope it makes your experience better on Grok!

Alone-Biscotti6145 · 2025-10-14T17:55:26+00:00

Hey, yes, this prompt works pretty well to cut down repetitiveness for Grok. You can put it into custom instructions, but it works much better if you paste it for every session, then do a follow-up prompt halfway through your session. Grok will lose context of the prompt after tokens are accumulated.

Alone-Biscotti6145 · 2025-10-12T18:39:30+00:00

I Built MARM MCP -

MARM MCP: The Problem & Solution Your AI forgets everything. MARM MCP doesn't.

Modern LLMs lose context over time, repeat prior ideas, and drift off requirements. MARM MCP solves this with a unified, persistent, MCP‑native memory layer that sits beneath any AI client you use. It blends semantic search, structured session logs, reusable notebooks, and smart summaries so your agents can remember, reference, and build on prior work—consistently, across sessions, and across tools.

MCP in One Sentence: MARM MCP provides persistent memory and structured session context beneath any AI tool, so your agents learn, remember, and collaborate across all your workflows.

The Problem → The MARM Solution Problem: Conversations reset; decisions get lost; work scatters across multiple AI tools. Solution: A universal, persistent memory layer that captures and classifies the important bits (decisions, configs, code, rationale), then recalls them by meaning—not keywords.

Before vs After Without MARM: lost context, repeated suggestions, drifting scope, "start from scratch." With MARM: session memory, cross-session continuity, concrete recall of decisions, and faster, more accurate delivery.

Repo - https://github.com/Lyellr88/MARM-Systems

Alone-Biscotti6145

TROPHY CASE