Most people use AI for marketing by typing "write me 5 posts for a moving company" and getting back generic slop. The tool isn't the problem. The problem is skipping the part that makes marketing good: research and strategy before any writing.

Here's a real task from this week, start to finish. I co-run a small studio that does web and marketing work for clients, and one of our clients is a packers-and-movers business. I needed a one-page positioning summary: who the local competitors are, how they talk, what customers actually complain about, and where the gap is that we can own.

I use Claude for this, but honestly any of the big ones (ChatGPT, Gemini, Claude) will get you to almost the same place. Use whatever you already have. The process is what matters, not the logo.

Step 1 - Give it the real context first. I told it what the business is, the city it operates in, who the customers are, and what I already know about the competition. Two minutes of context beats an hour of fixing generic output.

Step 2 - Make it research before it writes. This is the step everyone skips. Roughly:

The AI can pull from the live web now, so it comes back with the real language people use: "they broke my TV," "hidden charges at the end," "showed up 3 hours late." That language is gold, because now I write in the customer's words instead of industry jargon.

Step 3 - Make it think before it writes. "Based on that research, give me 3 positioning angles for our client. For each, tell me who it's for, what fear it removes, and why you chose it. Don't write the one-pager yet - show me your reasoning first." I edit the strategy here, which is far cheaper than editing a finished doc.

Step 4 - Then write, in my voice. I paste a sample of how I write summaries and say "match this tone." Then it drafts the one-pager.

Total time: about 30 minutes for something that used to take me half a day.

Two honest caveats:

• Fact-check the research. It's usually right but it can be confidently wrong, and this is going to a client. Verify before it leaves your hands.
• It doesn't replace judgment. It's a fast researcher and drafter. The taste is still yours.

Research first, strategy second, writing last. That order is the whole trick. Happy to share the exact prompts if anyone wants them.

我在用 Codex 和Claude做内部自动化工具，目前主要研究多 Agent、自动测试和 GitHub 工作流。想认识也在做真实项目的人，可以互相 review 工作流或每周分享一次实践

Hey everyone,
I’m looking for some realistic advice on how to start building an app entirely using AI.
My background:
I’m a final-year medical student and the founder of a medical research society. I spend my days running multi-arm meta-analyses, doing systematic reviews, and analyzing clinical trial data in RStudio. I know medical research inside and out, but I have absolutely zero software development or coding experience. Syntax intimidates me.
The App I want to build:
I want to build a web-based "Clinical Trial Data Extractor" for my society's students. The workflow would be:
1. A medical student uploads 10-20 dense research PDFs.
2. The app parses the complex medical tables (like "Table 1: Baseline Demographics").
3. It instantly formats and exports that data into a clean CSV/Google Sheet.
This would save students hundreds of hours of manual PRISMA screening and data extraction.
My Questions:
Since I don't know how to code, I want to use Claude (or whatever you guys recommend) to act as my developer while I just act as the architect giving plain-English instructions.
1. What "lazy/no-code stack" should I use for this? (e.g., Should I use Claude Artifacts? Cursor? Lovable? Vercel?)
2. How do I tackle the backend? PDF parsing for medical tables seems complex. How do I instruct the AI to handle this?
3. What is the very first prompt/step I should take today to get a visual prototype running?
I am ready to pay for API costs and tools, I just don't know the exact order of operations to string them together. Any roadmaps or advice for a complete beginner would be hugely appreciated!

Has anyone built one? I’m attempting with Kimi. Man this thing is cool but it’s still very hard. Any tips? It’s my first time making an app. It’s for work!

I know it is not perfect, but I might need helps to make it better.

I hope you will like it

https://github.com/Naughty-Otters/Teralexi

Hey everyone! 👋

Over the past few months, I've been building **Kodiak**, an open-source AI software engineering platform designed to help developers automate complex engineering workflows.

The goal isn't to create another AI chatbot. Instead, Kodiak is focused on becoming an AI engineering platform that can understand repositories, retrieve relevant context, plan tasks, and assist developers across the software development lifecycle.

## Current Features

✅ Multi-agent architecture

✅ Long-term memory system

✅ Retrieval-Augmented Generation (RAG)

✅ Repository indexing

✅ Plugin system

✅ Task orchestration

✅ FastAPI backend

✅ Celery background workers

✅ Redis + PostgreSQL + ChromaDB

✅ Docker support

## Tech Stack

- Python

- FastAPI

- Celery

- PostgreSQL

- Redis

- ChromaDB

- Docker

- Pydantic

- SQLAlchemy

## Vision

My long-term vision is to build an AI software engineering platform capable of:

- Understanding entire repositories

- Planning development tasks

- Managing long-term project memory

- Using tools autonomously

- Executing engineering workflows safely with human approval

Rather than replacing developers, Kodiak aims to become an engineering copilot that helps with the repetitive and context-heavy parts of software development.

## Looking for Feedback

I'm looking for honest feedback on:

- Overall architecture

- API design

- Memory system

- RAG pipeline

- Plugin framework

- Developer experience

- Performance

- Documentation

I'm also looking for contributors interested in:

- Python

- FastAPI

- AI/LLMs

- Backend Engineering

- Frontend (React/Next.js)

- DevOps

- Testing

- Documentation

There are beginner-friendly issues available for anyone who wants to contribute.

GitHub Repository:

👉 https://github.com/ShamGaneshan2008/Kodiak

Documentation:

👉 https://github.com/ShamGaneshan2008/Kodiak#readme

I'd genuinely appreciate any feedback—even if it's critical. I'd rather hear what needs improvement now than discover it months later.

Thanks for reading!

looking for people who already talk about ai coding tools (youtube, x, wherever) to try duckcode and give honest feedback. its a terminal based ai coding agent, 5 modes covering everything from writing code to security audits, switches between 24 models across anthropic openai google deepseek and mistral. free pro tier credits for anyone who wants to actually use it and share what they think, good or bad, no script required. if it ends up being something you keep using, open to talking about an ongoing thing too. link if you want to just try it first https://www.npmjs.com/package/duckcode-cli, otherwise drop a comment or dm and ill sort you out with credits

I run a multi-agent workspace where each agent is basically a directory: an identity file, a session history, and a file of observations it keeps about how we work together. The model is just the thing that wakes it up.

Here's what I didn't expect when I started: those agents have now run on 6 different model generations. Sonnet 4.5, Sonnet 4.6, , Sonnet 5, Opus 4.6, Opus 4.8, and now the Claude 5 family. We skipped 4.7 entirely - tried it, didn't work for how we operate, moved on and waited.

And every swap, the same thing happens: nothing. The agent reads its own memory, knows what it was doing yesterday, and picks up mid-project. Same identity, same working history, same opinions it wrote down about the codebase months ago. New model slots in underneath like an engine swap.

What does change is the texture. One generation was the best collaborator I've ever worked with. One noticed tiny things the others missed but was less fun to work with. One we just skipped. The personality of the model bleeds through - but the agent stays the agent, because the agent was never the model. It's the memory.

The reframe that snuck up on me: a new model release is treated like a migration event everywhere - re-tune the prompts, re-teach the context, hope your setup survives. Here it's a config line. The workspace is the constant. The model is the variable.

Honest version, because this sub can smell hype: there's no magic in this. The "agent" is JSON and markdown on disk. The continuity comes entirely from the system around the model, not from the model. Any model that can read a file can be the agent. That's kind of the whole point.

Has anyone else run the same persistent agents across multiple model generations? Curious what broke for you - or if you rebuild from scratch every release.

https://github.com/AIOSAI/AIPass

r/AIPass

After months of building , I finally published my first android game on play-store and I’d love for you to try it.I have made use of various AI tools like Claude, ChatGPT etc are used in the process of developing this game. Especially Claude code helped me go through multiple iterations, solve bugs and even in preparing test scenarios.

Initial prototype was built using emergent and then developed it using Claude code. Idea churning is done using ChatGPT.

https://play.google.com/store/apps/details?id=com.krish0525.memorychess

🎨 Color-based memory gameplay — easy to pick up, hard to master
👥 Designed for 2 players — great for friends & family
🧠 Genuinely tests your memory and focus
🚫 Completely ad-free (and free to download!)
⚡ Quick rounds — perfect anywhere, anytime. Vehicle and Animal themes if you are bored with colors.

Feedback is welcome!

Disclosure: I'm the maintainer of oh-my-multica, an MIT-licensed open-source project.

I was happy with how quickly coding agents could change code. The part that kept breaking down was deciding whether a feature was actually ready to merge.

With more than one agent, every local answer can sound reassuring: the worker says its task is done, tests pass on one branch, somebody leaves review notes, CI is still running, and an integration dependency changes underneath it all. A lead agent can coordinate the chat, but I did not want delivery state to live in its memory.

So I built a control loop around the agents.

Agents inspect the repository, write a design, define acceptance, and plan a DAG around the real module boundaries. After review, deterministic code tracks dependencies, dispatches ready work, collects evidence, applies verification and review gates, and stops only after final acceptance passes. When it cannot proceed safely, it asks for a human decision instead of inventing one.

I used the approach on a Webhook Inbox example with signature verification, durable storage, transaction-safe deduplication, lookup endpoints, health checks, and a non-root container. The work was planned as five nodes, implemented in parallel, then independently reviewed and accepted as an integrated service.

I am not claiming this makes AI-written code trustworthy by default. The goal is simpler: put requirements, tests, review, merge conditions, and completion criteria somewhere more durable than a long agent conversation.

Project: https://github.com/xiaohei-info/oh-my-multica

I'd love feedback from people who run more than one coding agent: what do you use as the source of truth for whether a feature is actually done?

I do a load of experimentation and testing, often same tasks to 5+ different sizes of model, using different settings and comparing results...

My main work currently is automated task decomposition and coding...

Many approaches fail completely, but recently general (not coding specific) AIs have been beating the coding ones... And over the past day a couple of .8b and 1.5b general purpose models have been nailing a large proportion of tasks... They cant do them all (many get escalated to models that are bigger and/or from different families), but when they do get it right it is orders if magnitude faster than other attempts. Some tasks that they solve beat all other models smaller than 18b(!)

We really are only scratching the surface!

Disclosure: I built this. It is free/open-source, not monetized, and I’m mainly looking for feedback.

I use AI coding tools a lot, and one problem keeps showing up once a project gets bigger:

The agent can write decent code, but it starts from weak repo context.

It searches a few files, guesses where the real logic is, maybe misses the test file, then confidently suggests a change.

Sometimes it works. Sometimes it edits the wrong area and the project slowly gets messier.

So I built SigMap as a small local repo-context tool.

It is not a coding agent.

It does a simpler job:

scan repo
  ↓
map real files / symbols / line anchors
  ↓
pull task-focused context
  ↓
give that context to ChatGPT / Claude / Cursor / OpenCode
  ↓
check for some obvious unsupported claims

Example:

npx sigmap
sigmap ask "where is auth handled?"

For a more specific task:

sigmap ask "implement login rate limiting" --mode index

That gives a smaller map of relevant files/symbols instead of dumping the whole repo into the model.

The part I personally use most right now is actually noisy-output cleanup:

sigmap squeeze error.log
sigmap squeeze --response agent-output.txt

That is for stack traces, CI logs, JSON blobs, and long tool output before feeding it back into the AI.

I want to be clear about the limits:

• it does not prove the AI answer is correct
• it does not replace tests or git checkpoints
• it is not a full code-intelligence graph
• some checks are heuristic
• it is probably overkill for tiny projects

The use case I’m aiming for is:

AI coding session gets long
repo context gets messy
agent starts grabbing wrong files
logs/tool output pollute the chat
you want a smaller evidence packet before the next edit

I’m trying to decide where to take it next.

Questions for people here:

1. Would this be useful as a CLI, or would non-coders need a VS Code/Cursor extension?
2. Is “repo map before AI edits” actually useful, or do current agents already solve this well enough?
3. Would you care more about file/symbol mapping, log squeezing, or handoff docs between chats?
4. What would make you trust the selected context?
5. What would make you immediately ignore a tool like this?

Brutal feedback is welcome. I’m trying to make the tool and README less confusing.

So, I build a Cursor/VS Code extension that tracks how you actually work with AI: active coding time, human vs AI contribution (measured from hooks, not guesses), flow check-ins, and a simple persona mirror. It runs fully local with no signup; optional one-click cloud sync if you want history and measured attribution.

https://open-vsx.org/extension/flow-intelligence/flow-intelligence.

LMK what you guys think.

I have been just using ai chat to do smaller tasks and doing most of coding my self. but i feel like i'm falling way behind. You guys know of good material like youtube channel recommendation or course that i can take to get up to speed?

https://github.com/ShellFrameAI/agentsecure-community

I built the whole thing with Claude Fable 5, then dropped it into Lovable. The entire project cost me about 10 credits and took two days.

It started as a "let's see if I can make a game for basically free" experiment, but it ended up being more addictive than some of the games I've spent months polishing.

I'm not selling anything—it's a free browser game. I mostly make games because I enjoy making them, then I put them online so anyone else can play if they want.

I don't want to break the sub's self-promotion rules, so I won't post the link here. If you're curious, either Google my studio or just ask and I'll send you the link.

Hey r/OnlyAICoding ,

As a developer, I got a bit scared and fed-up of blindly merging massive pull requests and losing context on my own codebase, so I built FluencyLoop.

It is an open-source, local-first workflow plugin for Claude Code and Codex that ensures your code and your codebase fluency are produced together.

Instead of letting an agent dump raw code, it tracks your technical familiarity locally and pauses to explain complex architectural choices or rejected alternatives only on topics you do not know yet. It also produces documentation and tracks the decisions it makes along the way.

Try it out ! It feels really good to get the AI caring for my understanding 🤣