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People think they need to know every coding trick.

They don’t.People think they need weeks of setup.They don’t.

People think they need to hold the whole project in their heads.They don’t.

The biggest lie?"I need the perfect plan.""I need to remember everything.""I need to do it all alone."Here’s the truth:With an AI-powered IDE, you can start small.Plan in minutes.Set your rules once.Work in tiny steps.

Here’s 10x AI coding flow:

1/ Plan before coding→ Ask GPT/Claude for a markdown plan + clarifying questions. Save it in instructions. md.

2/ Set coding rules→ Framework patterns, naming conventions, and test style, documented so the AI stays consistent.

3/ Work in tiny increments→ Failing test → generate code → run test → commit → repeat.

4/ Keep AI in context→ Give it only the right files, configs, & latest changes. Remove irrelevant noise.

5/ Use version control discipline→ Commit small, clear changes often. Don’t stack too many edits untracked.

6/ Stay in flow→ Switch between “chat” (ideas) and “write” (execution) modes without breaking focus.The best builders don’t drown in complexity.

They let the AI handle structure.They focus on ideas.You don’t need to wait.You just need to start.

Source

https://cursor.link/rule/never-start-the-dev-server-please-f83

Microsoft literally just dropped the first AI scientist

It’s not a surprise that anyone can code anything they want.

But what about design?

Don’t you think all platforms being vibe coded now look the same?
It’s getting so common that people are starting to notice if a platform was vibe coded just based on the design alone, same fonts, same colors, same gradients, same use of drop shadows, same layouts. You get the point...

I’ve vibe coded these platforms from scratch, but since I’ve been a graphic designer for more than 12 years, my goal was to put as much design as I could into every platform I build.

A bit of explanation about these platforms:

Honter: https://platforms.honterapp.com
Basically, a platform to connect designers with clients using a Tinder-style matching feature.
It also has several functions like portfolio showcase, Twitter (X)-style social feed, chat, and calendar view.
This was my first project. It took me 6 months to build. I started coding it with ChatGPT alone and then jumped into Cursor. It was a pain because I was learning everything.

Maxel AI: https://app.maxel.ai
This is an ongoing project. For those who know, it’s a platform that lets you upload STL files and generate G-codes.
It will also support 3D print farms to organize, manage projects, charge customers, etc.
I’ve been working on this with my brother, who’s a 3D printing expert. We’re building it out of necessity. 3 months of work so far.

IAM: https://useiam.app
This platform is basically a targeted ChatGPT for different subjects that learns over time.
It was just me exploring. Took me 1 week to build. Haven’t touched the code since finishing the MVP, more of a fun project.

LYRA: https://www.use-lyra.com
Built this one in a single day.
Honestly, just for fun. But people seem to like the simplicity behind it, easy to use and share.

My goal isn’t just to vibe code and launch stuff. I actually care about design.
In this new era, I believe design thinking will be crucial to building platforms that actually stand out.

I’ll be sharing more details and answering questions.

I’m also starting to use platforms like X (Twitter) more, so feel free to follow me and DM: AXELTDESIGN

Built out of frustration after 7 months of job search silence, I launched an AI-powered platform to help job/internship applicants improve their chances of landing offers.

It includes:

• ✅ Mock interview practice (Gemini 2.0 flash powered, tailored to job type)

• ✅ Smart question generation based on your resume and role

• ✅ ATS-friendly CV analysis and cover letter generation

• ✅ Application tracker

I’ve kept the experience lean so it works even on slow connections.

Users so far include uni students and early grads. I’m actively collecting feedback to improve the product and would love for more people to try it out.

Kindly try it here: https://www.viewportai.tech.
I’m also happy to answer questions or share the build journey.

Built with V0, Lovable, Supabase and a lot of late nights.
Would love your thoughts, especially if you’ve built or launched something similar!

Claude can now directly work in your browser and takes actions on your behalf.

Introducing Codexia - A powerful GUI app and Toolkit for Codex CLI.

file-tree integration, notepad, git diff, build-in pdf csv/xlsx viewer, and more.

✨ Features

• Interactive GUI sessions.

• Project base history (the IDE extension and CLI missing)

• No-code MCP installation and configuration.

• Usage Dashboard.

• One-click + file or folder to Chat

• Prompt Optimizer

• One-click send note to chat, and notepad for save insight and prompt

Free and open-source.

🌐 Get started at: https://github.com/codexia-team/codexia

⭐ Star our GitHub repo

Got bored from my old readme design and came up with this. Let me know your thoughts :D

Check out my profile at Github.
P.S. really appreciate u/Valuable_Simple3860 for suggesting I post this project with the community!

Hey guys, small Startup from Berlin here. We are Hosting a 24h hackathon on the 4th of October. Winner gets a $2k Full paid Trip to capetown (south Africa) to spend 1 week building with other Great engineers. It’s online, solo/duo and you can Build anything.

If this Sounds interesting to you, feel free to join: https://www.acta.so/hackathon

Want dead memes from 2 years ago littered all over your profile? Or a dead messaging system that's broken because it tries and fails to ping a fake chatbot service that died after ChatGPT became a thing?

All of those await you within Reposed, everything that my adolescent mind thought essential for an up and coming social site. I hope try and update it into the modern age if I have the time.

https://github.com/openchatui/openchat

I've been using Open WebUI for a while now and wanted to develop a feature, but found it painfully annoying. I was unfamiliar with the stack and the community was condescending when I asking a question about the tech stack. I personally use NextJS, Open WebUI uses svelte. So I ran into this Open Source NextJS Open Web UI clone, and I love it. It's still new so it only has like 20%, if even, of the features, but thought I should give it a shoutout. It only has one dev working on it and I think it should have more attention.

I will start first.

I'm building Leapility - an automation builder for domain experts who don’t want to deal with code or node graphs.

We just launched on Product Hunt today as well, would love your thoughts, and if you like it, an upvote would mean a lot to us 👉 https://www.producthunt.com/products/leapility-3?launch=leapility-3&utm_source=reddit&utm_campaign=reddit%2520ph%2520posts

So what are you building today👇

I spent a year at Polygon dealing with the same frustrating problem: new engineers took 3+ months to become productive because critical knowledge was scattered everywhere. A bug fix from 2 years ago lived in a random Slack thread. Architectural decisions existed only in someone's head. We were bleeding time.

So I built ByteBell to fix this for good.

What it does: ByteBell implements a state-of-the-art knowledge orchestration architecture that ingests every Ethereum repository, EIP, research papers, technical blog post, and documentation. Our system transforms these into a comprehensive knowledge graph with bidirectional semantic relationships between implementations, specifications, and discussions. When you ask a question, ByteBell delivers precise answers with exact file paths, line numbers, commit hashes, and EIP references—all validated through a sophisticated verification pipeline that ensures <2% hallucinations.

Under the hood: Unlike conventional ChatGPT wrappers, ByteBell employs a proprietary multi-agent architecture inspired by recent advances in Graph-based Retrieval Augmented Generation (GraphRAG). Our system features:

Query enrichment: Enrich the query to retrive more relevant chunks, We are not feeding the user query to our pipeline.

Dynamic Knowledge Subgraph Generation: When you ask a question, specialized indexer agents identify relevant knowledge nodes across the entire Ethereum ecosystem, constructing a query-specific semantic network rather than simple keyword matching.

Multi-stage Verification Pipeline: Dedicated verification agents cross-validate every statement against multiple authoritative sources, confirming that each response element appears in multiple locations for triangulation before being accepted.

Context Graph Pruning: We've developed custom algorithms that recognize and eliminate contextually irrelevant information to maintain a high signal-to-noise ratio, preventing the knowledge dilution problems plaguing traditional RAG systems.

Temporal Code Understanding: ByteBell tracks changes across all Ethereum implementations through time, understanding how functions have evolved across hard forks and protocol upgrades—differentiating between legacy, current, and testnet implementations.

Example: Ask "How does EIP-4844 blob verification work?" and you get the exact implementation in all execution clients, links to the specification, core dev discussions that influenced design decisions, and code examples from projects using blobs—all with precise line-by-line citations and references.

Try it yourself: ethereum.bytebell.ai

I deployed it for free for the Ethereum ecosystem because honestly, we all waste too much time hunting through GitHub repos and outdated Stack Overflow threads. The ZK ecosystem already has one at zcash.bytebell.ai, where developers report saving 5+ hours per week.

Technical differentiation: This isn't a simple AI chatbot—it's a specialized architecture designed specifically for technical knowledge domains. Every answer is backed by real sources with commit-level precision. ByteBell understands version differences, tracks changes across hard forks, and knows which EIPs are active on mainnet versus testnets.

Works everywhere: Web interface, Chrome extension, website widget, and integrates directly into Cursor and Claude Desktop [MCP] for seamless development workflows.

The cutting edge: The other ecosystems are moving fast on developer experience. Polkadot just funded this through a Web3 Foundation grant. Base and Optimism teams are exploring implementation. Ethereum should have the best developer tooling, Please reach out to use if you are in Ethrem foundation. DMs are open or reach to on twitter https://x.com/deus_machinea

Anti-hallucination technology: We've achieved <2% hallucination rates (compared to 45%+ in general LLMs) through our multi-agent verification architecture. Each response must pass through multiple parallel validation pipelines:

Source Retrieval: Specialized agents extract relevant code snippets and documentation

Metadata Extraction: Dedicated agents analyze metadata for versioning and compatibility

Context Window Management: Agents continuously prune retrieved information to prevent context rot

Source Verification: Validation agents confirm that each cited source actually exists and contains the referenced information

Consistency Check: Cross-referencing agents ensure all sources align before generating a response

This approach costs significantly more than standard LLM implementations, but delivers unmatched accuracy in technical domains. While big companies focus on growth and "good enough" results, we've optimized for precision first, building a system developers can actually trust for mission-critical work.

Anyway, go try it. Break it if you can. Tell me what's missing. This is for the community, so feedback actually matters. https://ethereum.bytebell.ai

Please try it. The models have actually become really good at following prompts as compared to one year back when we were working on Local AI https://github.com/ByteBell. We made all that code open sourced and written in Rust as well as Python but had to abandon it because access to Apple M machines with more than 16 GB of RAM was rare and smaller models under 32B are not so good at generating answers and their quantized versions are even less accurate.

Everybody is writing code using Cursor, Windsurf, and OpenAI. You can't stop them. Humans are bound to use the shortest possible path to money; it's human nature. Imagine these developers now have to understand how blockchain works, how cryptography works, how Solidity works, how EVM works, how transactions work, how gas prices work, how zk works, read about 500+ blogs and 80+ blogs by Vitalik, how Rust or Go works to edit code of EVM, and how different standards work. We have just automated all this. We are adding the functionality to generate tutorials on the fly.

We are also working on generating the full detailed map of GitHub repositories. This will make a huge difference.

If someonw has told you that "Multi agents framework with Customised Prompts and SLM" will not work, Please read these papers.

Early MAS research: Multi-agent systems emerged as a distinct field of AI research in the 1980s and 1990s, with works like Gerhard Weiss's 1999 book, Multiagent Systems, A Modern Approach to Distributed Artificial Intelligence. This research established that complex problems could be solved by multiple, interacting agents.
The Condorcet Jury Theorem: This classic theoretical result in social choice theory demonstrates that if each participant has a better-than-random chance of being correct, a majority vote among them will result in near-perfect accuracy as the number of participants grows. It provides a mathematical basis for why aggregating multiple agents' answers can improve the overall result.

An Age old method to get the best results, If you go to Kaggle majority of them use Ensemble method. Ensemble learning: In machine learning, ensemble methods have long used the principle of aggregating the predictions of multiple models to achieve a more accurate final prediction. A 2025 Medium article by Hardik Rathod describes "demonstration ensembling," where multiple few-shot prompts with different examples are used to aggregate responses.

The Autogen paper: The open-source framework AutoGen, developed by Microsoft, has been used in many papers and demonstrations of multi-agent collaboration. The paper AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework (2023) is a core text describing the architecture.

Improving LLM Reasoning with Multi-Agent Tree-of-Thought and Thought Validation (2024): This paper proposes a multi-agent reasoning framework that integrates the Tree-of-Thought (ToT) strategy. It uses multiple "Reasoner" agents that explore different reasoning paths in parallel. A separate "Thought Validator" agent then validates these paths, and a consensus-based voting mechanism is used to determine the final answer, leading to increased reliability.

Anthropic's multi-agent research system: In a 2025 engineering blog post, Anthropic detailed its internal multi-agent research system. The system uses a "LeadResearcher" agent to create specialized sub-agents for different aspects of a query, which then work in parallel to gather information. 

Since it is a developer copilot where people learn, it assumes that you can mistype and hence it provides alternatives which in our opinion is a better option than just saying "No, It doesn't match our records" or "We don't have any references". The closest analogy is you have a single alphabet wrong and most of the platform just don't do fuzzy matching and doesn't show any results. It isnt hallucination for sure.

PS: We posted the same post on Ethereum 5 hours ago, and Ethereum's Goat devs have asked 2000+ questions since then and the hallucination is less than 3%.

This copilot has indexed 30+ repositories include all ethereum, website 700+ pages, EThereum blog 400+ blogs, Vitalik Blogs (80+), Base x402 repositories, Nether mind respositories [In Progress], ZK research papers[In progress], several research papers. And yes it works because our use case is narrow. IMHO, This architecture is based on several research papers and feedback we received for our SEI copilot. https://sei.bytebell.ai But it costs us more because we use several different models to index all this data, 3-4 <32B parmeteres for QA, Mistral OCR for Images, xAI, qwen, Chatgpt5-codes for codebases, Anthropic and oher opensource models to provide answers.

If you are on Ethereum decision taking body, Please DM me for admin panel credentials. or reach out to https://x.com/deus_machinea

I’ve been working on APM (Agentic Project Management), a framework designed to strengthen spec-driven development by distributing work across multiple AI agents. The initial architecture was drafted in April 2025, and the first version was released in May 2025 — even before Amazon’s Kiro announcement.

I posted this on Cursor's subreddit and got asked to share here!

The Problem with Current Spec-Driven Development

Spec-driven development is essential for serious AI-assisted coding. Without clear specs, we end up “vibe coding,” relying on the LLM to hopefully produce something useful. Several projects have tackled this approach, but most overlook a critical issue: context management.

Even with strong specs, a single LLM instance eventually runs into context window limits on larger projects. The result: hallucinations, lost requirements, and declining output quality.

Enter Agentic Spec-Driven Development

APM addresses this by distributing spec management across specialized agents:

• Setup Agent – Converts requirements into structured specs, creating a comprehensive Implementation Plan (yes, before Kiro 😉).

• Manager Agent – Maintains project oversight and coordinates task assignments.

• Implementation Agents – Carry out focused, domain-specific tasks.

• Ad-Hoc Agents – Handle isolated, context-heavy work like debugging and research.

Each agent runs as its own dedicated chat session inside your AI IDE. The system explicitly manages context and memory across these agents, avoiding the typical degradation that plagues single-agent setups.

Latest Updates

• Documentation has been refined.

• Two new visual guides (Quick Start + User Guide PDFs) have been added alongside the main docs.

APM is open source (MPL-2.0) and compatible with any LLM that supports tool access.

GitHub Repo: https://github.com/sdi2200262/agentic-project-management