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Products that do what I built a GPT-4 bot which builds software incrementally does
The ability to synthesize a relatively short snipped of code was already demonstrated. But I thought it would be interesting to test whether GPT-4 can replace a programmer completely. To do that, AI needs to plan its actions and work on code incrementally, one piece at a time. The challenge is the context size: the entire code base + plan does not fit into the context. My approach: Add only relevant parts of the code base to the context. Specifically, AI generation engine implements two distinct phases: planning and coding. In the planning phase, GPT-4 receives a tree of tasks and a summary…
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Deep Work Plan▲114Models matter. Context matters more. Give your agent a plan.
Jun 2026 · deepworkplan.com
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As a solo developer on a sizeable project, I found myself facing the challenge of creating documentation, a task I admittedly do not enjoy. Fortunately, ChatGPT arrived just in time. After experimenting with it, I discovered that it generates high-quality documentation for code files, even better than what I could produce manually. This realization led me to develop Code-Narrator, a client that simplifies the documentation process. Code-Narrator (https://github.com/ingig/code-narrator) utilizes GPT-4 to analyze your code files and automatically generate documentation. The…
2023 · github.com
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Hello all, I'm a software developer. Over the last few months more and more of my work has turned into using coding agents instead of typing the whole code myself. Usually a few claude sessions at once, sometimes codex, one per feature or per revealed bug. I ran them in a split terminal for a few weeks, and quickly spotted two main problems. The first is that I couldn't easily tell which agent was stuck waiting on me and which was still working, so I'd cycle through sessions and checking on them. The second one: agents sharing a single branch step on each other. Two of them could be editing…
Jul 2026 · shikigami.dev
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AI changed the way we code, but we're still using the old processes, and we've become the bottleneck, the AI is waiting for us - to reply, to open our laptops, to review the code, and so on. We're building the future of AI software development. The agents are autonomous, they run in sandboxes, automatically fix the pipelines, and deliver you the final, working code. You can use live preview to see the changes they made. Working across multiple repositories, all within the same session. This is the future - you don't need an IDE, and you don't have to run anything locally.
Mar 2026 · agenhq.com
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The main goal of this was to be able to not just run multiple Claude Code sessions at once, but actually manage them and keep track of what I was doing. Sometimes this is multiple attempts on the same task, sometimes I work several tasks at once. Really I was just sick of twiddling my thumbs waiting for the coding agent to finish, and I wanted it to be easy to work on/review/test another change while I waited.
2025 · github.com
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Hey HN, After GPT-3 created waves in the tech industry, a lot of AI tools were emerging and with that, some AI website builders But the results seemed way too generic to us. It felt like the developers were rushing to catch the wave instead of building a proper tool We took our time, did months of RnD and finally came up with something better than what others in the market are doing. It’s got better design output. While it’s still in beta, I wanted to show HN what we did. Will appreciate the feedback when you guys try it out. Here is the link to signup for the beta:…
2024 · dorik.com
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We've built the first General AI Agent that works seamlessly across multiple AI platforms, e.g. ChatGPT, Claude, Cursor, and more. Try it today at no cost (no API credits needed), and join our waitlist for Flow, our visual designer that lets you customize it or build your own agent using just natural language—no coding required. #Why we made this We built this after experiencing firsthand the frustration of designing AI agents that require coding or the use of platforms with steep learning curves, only to find ourselves tied to these solutions. Our team spent months in stealth developing a…
2025 · orkestralai.com
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I built CodeLens.AI - a tool that compares how 6 top LLMs (GPT-5, Claude Opus 4.1, Claude Sonnet 4.5, Grok 4, Gemini 2.5 Pro, o3) handle your actual code tasks. How it works: - Upload code + describe task (refactoring, security review, architecture, etc.) - All 6 models run in parallel (~2-5 min) - See side-by-side comparison with AI judge scores - Community votes on winners (blind voting) - Each evaluation gets reflected in the overall AI model leaderboard, showing us best ones Why I built this: Existing benchmarks (HumanEval, SWE-Bench) don't reflect real-world developer tasks. I wanted to…
Oct 2025 · codelens.ai
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I come from a machine learning background - PyTorch code, leaving a training job running overnight, and Jupyter Notebooks. I hadn't touched much frontend before diving deep into start-ups. It was similar for my co-founder Nick, who spent time working on semiconductors. I started building, and noticing patterns in AI outputs. Enough to be able to understand how a hook works, how to manage state and why Typescript is great. But whenever it came to optimising a piece of code, debugging state issues or designing a codebase from scratch, my mind went blank. I went to ChatGPT Study Mode to seek…
Apr 2026 · chestnut.so
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We wanted to test if a smaller model like GPT-4.1-mini could beat its bigger brother 4.1 at the game Tic-Tac-Toe using only context engineering. We put them in a 100-game tournament. For the smaller model, we gave it a few examples of winning moves from past games right before it made its own move. The results were clear. Without the examples, the smaller model struggled against GPT-4.1. With the examples, its effectiveness increased by nearly 200%, and it consistently won. It's a simple demonstration, but it shows that a smaller, faster model with good, timely examples can outperform a more…
2025 · github.com
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For the last 6 months, I've been building ORUS Builder, an open-source AI code generator. My goal was to fix the biggest issue I have with tools like v0, Lovable, etc. – they generate broken, non-compiling code that needs hours of debugging. ORUS Builder is different. It uses a "Compiler-Integrity Generation" (CIG) protocol, a set of cognitive validation steps that run before the code is generated. The result is a 99.9% first-time compilation success rate in my tests. The workflow is simple: 1.Describe an app in a single prompt. 2.It generates a full-stack application…
Nov 2025
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