u/Jazzlike-Form9669

For very long i was using claude-code, i will not go deeper into how bad this is and how much i wasted my time on it but in search for better coding harness, i came across one video on youtube - it was maximilianzuern talking about special kind of coding harness called PI - minimal with very very limited sets of tools. I saw that video and decided to give it a try - it was good and interesting but it was same as other following same principal and design. Yes i have gone through source code line by line.

My concern :-

Why coding agents has to support terminal by default, we spent our life working in browser, it is optimised for good data presentation - be it graph , beautiful card and so on. Why try to limit display and presentation capabilities of LLM to terminal only. Why not directly go with browser where human can better interact with agents.

Same agent loop design and context management , why sends all session history per turn as context - this is what it is and being followed by all harness out there be it claude code , opencode , PI or others, same story. Agree it or not but this burn tokens like water , choking LLM context , reaching limit too quickly and hours of wait time in case of claude-code.

Why treat plan mode as secondary features , agree or not but we all know how we are using this harness in real world, we ask it to build one feature ( sometime more than one ) - and it blindly start working on the project ( yes it will ask you needed question to better understand your intent & you can also ask to plan it first ), and in few hours it will generate massive thousands lines of change and eventually you will have massive PRs to review- if you are working with critical software ( banking, finance, hospitality and security ) - you cannot skip this PRs review - but PRs with thousand of lines of code , man nobody is going to review it no matter how skilful one is.

Slow linear progression on feature , like seriously why ? Why we are waiting for hours setting in-front of claude-code waiting just to click ENTER ( we know this feeling - setting scroll reels waiting to press ENTER )??

We are doing this bcz, either we are not sure what files claude will touch in advance and will do what modifications to achieve your feature or you are not reading plan carefully ( reason - treating it as secondary feature ).

- My efforts so far ( OGCODE )

Well i will not go deep ranting about coding harness of current days. Let me give you some context on what i was building for months , I named it ogcode-it is MIT licensed and free to use and distribute.

Whatever i told you so far, all drawbacks, ogcode is solving all of them. But let me give you few things i love about ogcode, first thing first ogcode treat plan mode as default - you first plan your features or bugs fix with planner agent, once satisfied - click LOCK PLAN cta and task planner agent will break this plan into multiple parallel merge/DAGs safe tasks, then you can assign suitable coding agent to each tasks ( this can be automatic as well, according to task kind and complexity ). Let's say some task is UI heavy and some BL heavy then you assign agent suitable for UI/UX and to others agents good in BL. You can manage and maintain pool of coding agents according to their skills set and experience in subjects matters as well as assign one reviewer ( high end model ) per executor agents. Reviewer will give rating and review after each task completion , that these coding agent can use to correct itself and perform better next time. If can see - we are benchmarking LLMs on real project with real tasks in real world - we will release this benchmark weekly ( not decided yet ).

- Agent Loop Memory

This i need to tell you for sure, it is called agentic session memory , it is built on idea- give each turn limited context necessary to perform given user query. This way ogcode right now is saving almost 70% tokens in longer session in my testing as compared to other out there - but knows that it is also improving ogcode accuracy. How? More context doesn't means higher accuracy it will add extra noise to LLM , it is relevant and to the point context that is needed to achieve query goal is what improves accuracy. There is too much to read in source code and README file, please have a look at my repo for further reading.

- Agentic Notes

Imagine you are exploring codebase , how things are implemented, how things work , how features are connected to each other etc.. Most of the time we find our self asking same question again and again regarding features and architecture , but now in ogcode - you can save to notes your how to query and start building project knowledge base, so next time - when you are implementing new features , or fixing bugs agents can recall your notes knowledge base for accurate and faster lookup instead of wasting tokens again to understand how things works inside your projects using grep and glob. Believe me this sounds simple but can save you lots of tokens and keep project on decided path. Best part these generated notes keep it's self upto date as projects grows. There is lots to it's plzz check repo for more details. This one feature makes ogcode goto tool for new repo understanding and exploration.

I these 100x founder need different kind of coding agent - one that can actually save tokens instead of wasting it like water 🤥. For sure not claude-code ( evil )

I am building ogcode ( a coding agent ) , written completely from scratch in go + solidJs, In ogcode there is a feature called deep_search ( which is essentially a LLM tool exposed to agent to do deep research on live internet ). Thing is i tried all possible way ( go routine per page fetch and extractions etc ) but it is still taking more than 15 to 45 sec in some case even more. If you can suggeste me some idea around this to speed up , please do share. Here is link : https://github.com/prasenjeet-symon/ogcode

Lately we have seen many closed sourced and open source coding harness getting tractions, let see which one is more popular that other.

For me i would go with -

https://github.com/earendil-works/pi ( Superb lightweight and highly customisable )

https://github.com/prasenjeet-symon/ogcode ( Minimal + It's agentic session memory that is saving me much tokens - almost in just 10 mins of session saved me 3M tokens )

For very long i was using claude-code, i will not go deeper into how bad this is and how much i wasted my time on it but in search for better coding harness, i came across one video on youtube - it was maximilianzuern talking about special kind of coding harness called PI - minimal with very very limited sets of tools. I saw that video and decided to give it a try - it was good and interesting but it was same as other following same principal and design. Yes i have gone through source code line by line.

My concern :-

Why coding agents has to support terminal by default, we spent our life working in browser, it is optimised for good data presentation - be it graph , beautiful card and so on. Why try to limit display and presentation capabilities of LLM to terminal only. Why not directly go with browser where human can better interact with agents.

Same agent loop design and context management , why sends all session history per turn as context - this is what it is and being followed by all harness out there be it claude code , opencode , PI or others, same story. Agree it or not but this burn tokens like water , choking LLM context , reaching limit too quickly and hours of wait time in case of claude-code.

Why treat plan mode as secondary features , agree or not but we all know how we are using this harness in real world, we ask it to build one feature ( sometime more than one ) - and it blindly start working on the project ( yes it will ask you needed question to better understand your intent & you can also ask to plan it first ), and in few hours it will generate massive thousands lines of change and eventually you will have massive PRs to review- if you are working with critical software ( banking, finance, hospitality and security ) - you cannot skip this PRs review - but PRs with thousand of lines of code , man nobody is going to review it no matter how skilful one is.

Slow linear progression on feature , like seriously why ? Why we are waiting for hours setting in-front of claude-code waiting just to click ENTER ( we know this feeling - setting scroll reels waiting to press ENTER )??

We are doing this bcz, either we are not sure what files claude will touch in advance and will do what modifications to achieve your feature or you are not reading plan carefully ( reason - treating it as secondary feature ).

My efforts so far

Well i will not go deep ranting about coding harness of current days. Let me give you some context on what i was building for months , I named it ogcode-it is MIT licensed and free to use and distribute.

Whatever i told you so far, all drawbacks, ogcode is solving all of them. But let me give you few things i love about ogcode, first thing first ogcode treat plan mode as default - you first plan your features or bugs fix with planner agent, once satisfied - click LOCK PLAN cta and task planner agent will break this plan into multiple parallel merge/DAGs safe tasks, then you can assign suitable coding agent to each tasks ( this can be automatic as well, according to task kind and complexity ). Let's say some task is UI heavy and some BL heavy then you assign agent suitable for UI/UX and to others agents good in BL. You can manage and maintain pool of coding agents according to their skills set and experience in subjects matters as well as assign one reviewer ( high end model ) per executor agents. Reviewer will give rating and review after each task completion , that these coding agent can use to correct itself and perform better next time. If can see - we are benchmarking LLMs on real project with real tasks in real world - we will release this benchmark weekly ( not decided yet ).

This i need to tell you for sure, it is called agentic session memory , it is built on idea- give each turn limited context necessary to perform given user query. This way ogcode right now is saving almost 70% tokens in longer session in my testing as compared to other out there - but knows that it is also improving ogcode accuracy. How? More context doesn't means higher accuracy it will add extra noise to LLM , it is relevant and to the point context that is needed to achieve query goal is what improves accuracy. There is too much to read in source code and README file, please have a look at my repo for further reading.

Pasting my GitHub repo link in here  https://github.com/prasenjeet-symon/ogcode 

References :-

During my research i came across these videos:-

by Sally-Ann Delucia on Context Management in Agents- such a good watch , she really knows her field - https://www.youtube.com/watch?v=esY99nYXxR4

by Luke Alvoeiro on The Multi-Agent Architecture That Actually Ships - https://www.youtube.com/watch?v=ow1we5PzK-o