brainless

I started building an app with similar goals but with the very different approach. I work on my own coding agent, https://github.com/brainless/nocodo, where I have been trying to build a provenance based engine that will generate or modify prompts to point to the decisions that a team has made. That work is in the branch: feature/praxis_agent_runtime

While working on this I figured what if I build a proxy for coding agents - Claude Code, opencode, Codex, etc. support a proxy. This proxy would edit prompts and tool_calls and feed context from an internal index it will maintain. That index will contain git logs, GitHub/JIRA/etc tickets/epics, PRD or other documents, tech stack setup.

It is just an idea and may not work but working at the proxy layer means this can be deployed at a team level, needs no MCP install and can re-shape prompts for everyone depending on the project. Wild idea perhaps.

That is a good point. I do think these models would be good in the decision making. The large models are trained to use tool calling. Perhaps the small models can generate the text that would express their decision but not generate good JSON to reply with correct syntax. I do not know but this is my hunch.

I recently came across this model and I would love to try it with my coding agent soon.

I really like the idea of small models that can reason but do not have too much knowledge. Also, no emphasis on tool calls. I think the agent should do the heavy lifting and reach half way.

I use really small models, like Qwen 3.5 0.8B to 9B - no tool calling, no MCP, no skills, nothing. No multi-turn chat even. Models are given very specific tasks using a vast number of system prompts and all the response handling is done in the agent(s).

https://github.com/brainless/nocodo

I am not a language nerd but I keep on experimenting in my own ways to use the type system to generate code that is more reliable.

I build a coding agent specifically for small models, which makes everything harder. I started this chat with Claude to build the next step: https://claude.ai/share/4264e5f6-b334-426c-afe4-904d233ef946 - how can I go from PRD to a typed representation of the business logic.

The I started building as per https://github.com/brainless/nocodo/blob/feature/praxis_agen.... The praxis crate: https://github.com/brainless/nocodo/tree/feature/praxis_agen... and a sample Todo app: https://github.com/brainless/nocodo_example_todo_app

Generating unit tests for the library functions of any project would be done via a separate agent than the one coding the functions. And then use tree-sitter to statically check code to PRD (provenance graph).

Again, not a language nerd, just enjoying chasing a goal.

I have been building a coding agent for small and even tiny LLMs, local inference. I experiment with Qwen 3.5 0.8B but that is too tiny. 4B is a better one for most of my needs. I mix with 9B and then up to 20B models (not on my computer).

It builds on an opinionated tech stack - Rust (Actix Web, Diesel, SQLite) and Typescript (Solid, DaisyUI). There are multiple agents which play roles like PO, PM, Architect, Rust Engineer, Typescript Engineer and so on.

The idea is to go from user prompts to Epics/Tasks - PO/PM do this. Then to go from Tasks to YAML or similar syntax (I have not figured this out yet) and break into Rust and Typescript code dependencies.

I am focusing on the Rust side: how can small models write Model, Controller, Router, User/Permission and custom business logic in helper functions (called from Controller or BackgroundTask). Building a set of types to express business logic, for example in https://github.com/brainless/nocodo/blob/feature/praxis_agen...

Then I will use tree-sitter to build a graph of which business logic (in the helper functions) correspond with which provenance (source of truth given by user).

There is no tool calling for most of the agents, no MCP, no multi-turn chats. Most of the code writing agents one-shot the response with a lot of code reference in their prompts.

https://github.com/brainless/nocodo

This is good and terrible. The extra effort a model has taken is good but the way to do it is terrible. Tasks that can use a lot of deterministic paths and some creative (generative AI) paths are being turned into tokemaxxing strategies.

Browser automation, code comprehension, git management, code change, running commands - everything has simpler tooling that we could have built instead of a model first approach. A deterministic loop with thousands of catches and effective use of generative AI would also look "proactive". Instead we let the model run the tools, where tools have no context themselves.

That is why companies are creating bigger models and thinner deterministic agents to create awe and earn $ when we could go the other way and make much of these possible on local inference even.

I believe we can build a "proactive" but much, much more deterministic system with smaller models. I hope I am not the only one chasing this, here is my approach: https://github.com/brainless/nocodo

I agree with the core idea. I am building a coding agent for non-engineers on small/local LLMs only.

Commits are great but for an agent that works for the non-engineers (what I am building), commits cannot be represent the chain of thought since that chain has happened before - in English and has lots of debate/discussion with agents.

Zed is focused on engineers. I am focused on everyone else but I came to similar conclusions since my situation is tighter. Software has to be defined by humans, then codified (in development) by agents and then tested by humans. Deployment, rollbacks, etc. also has to come into the scene.

I have been breaking down the conversation into agents playing roles like PO, PM, Architect, etc. Then taking outputs from those into structured inputs for Rust engineer, Typescript engineer and so on. It is all WIP but holding on to this chain of "chats" is key in my opinion. That is where the software is made.

https://github.com/brainless/nocodo

Thanks! And yes, an "architect" agent will be needed to close the loop.

Hey folks, I have been experimenting on something that I am still not sure will work but it just might!

I started off building a generic coding agent but focused on small/medium models, like GPT OSS 120B or 20B or models in between. I felt the early days of LLMs, where we would share example input/output may work if the prompt and context was very specific. This led me to experiment with local models. I have tried up to 9B models on my M4 Mac Mini 16GB and currently I am trying with Qwen 3.5 0.8B.

The idea is that a specific agent only focuses on a specific part of a typical full-stack CRUD app - say the model layer of MVC. I am using Diesel with Rust, so an agent specific to that handles the prompt to model struct, Diesel schema and function blocks for the Rust impl. The agent actually has separate modes with separate prompts for each of these 3 parts. It is given examples from official docs and creating new examples is trivial.

Here is an example: https://github.com/brainless/nocodo/blob/main/agents/src/rus...

The Rust engineer agent with Diesel modes works and there is deterministic code to glue all the generated code together. The next step would be to run Rust/cargo format, compiler or other tools deterministically. If there are errors, there would be specific agents, perhaps using slightly bigger models (<10B) to pass errors and code as a prompt. Again, we can supply lots of contextual examples from official docs.

I do not know if this setup will scale to accommodate all parts of typical MVC, routing, frontend (Solid state/context, forms, views), design (Tailwind based components) but it just might. This is a passion project that is going on slowly. I thought there is enough work done to share.

Please feel free to look through the code. Again, it is a WIP. Eventually this will be a desktop app working with llama.cpp.

Cheers!

Thank you! I am not a researcher, I am a software engineer and I have been chasing better harness for quite some time now.

I firmly believe that we can bring down the costs for much of our productivity needs by a huge factor if there are guardrails. This is how I am building my coding agent: https://github.com/brainless/nocodo

There is so much we can do if we create tools that do more heavy lifting. Your example of ToolResolutionError is something I have not thought of. Again, I am coming at this from software engineering background, I still do not understand much of the inner working of models or their inference layer but I am sure I will slowly create a coding agent that performs really well for majority of people/business use cases (not enterprise) with small models and big harness.

If coding is such a big part of LLM agents' usage at the moment, I do not understand how far the best models will continue to shine and take the largest chunk of revenue. I am far away from tech hubs but I think better harness will utilize smaller models for more constrained, efficient and reliable coding agents.

In a way this is like distilling (but it is not) but you can make better harness (tackle more edge cases, better tool/function definitions, sandbox handling, bash management, DB management, deployment management, etc.) but extracting what LLMs know into code.

Maybe I am wrong but I would like to see custom software for the last mile (tiny/small businesses) becoming a reality. AI would eat the world of software but costs would go down since you can extract value upstream from the LLMs and spread downstream through tighter coding agents.

I am building a coding agent that will not be small - it will be a lot of code, carefully mixed roles (mimic a software dev shop) with separate tools available to different roles. And all this code is generated by other coding agents. https://github.com/brainless/nocodo

I am a nobody from nowhere with 18 years of software engineering behind me. I do not care about revenue. I just want to see a regular business owner's workflow going live on their own VPS.

I really believe that the Spreadsheets UX is great for mainstream users and that is what drives me for my coding agent that uses the sheets UX: https://github.com/brainless/nocodo

Super early stage but I am really happy to read your comment.

Most of my own products are GPLv3 licensed. There are a few with MIT but I may switch to GPLv3. I want to make money with hosting though.

Desktop apps are with Tauri, so they are also web apps if/when I sell hosting.

I will give it a shot. I am very happy to see other projects where people are trying to build with small models.

I have to check how you are using state machines but I have also been focused on small models for a while now.

nocodo is one of my product experiments, currently using 120B model but I have tested a few agents inside it with 20B models.

I create a bunch of agents, each with very specific goals. Like Project Manager, Backend Engineer, etc.

Each agent gets a very compact list of tools and access to only certain parts of the filesystem or commands.

https://github.com/brainless/nocodo/tree/main/agents/src

Lovely to see the push for tiny models.

I have been building for small (20B or less) models for quite a while. Highly focused/constrained agents, many of them running together in some kind of task orchestration mode to achieve what feels like one "agent".

I build (privacy first) desktop apps this way and I want to get into mobile apps with similar ideas but tiny models.

I build all my projects with Rust and Typescript (https://github.com/brainless). I had started learning Rust around 2023 but was progressing very slow. Since I left writing (or even reading) code line by line about a year ago, I build exclusively with Rust and Typescript. API types are generated from Rust. All my projects have a shared-types folder with a utility to generated Typescript types. I have a template that I use for each of my projects: https://github.com/brainless/rustysolid.

I am from a Python background (11 years or so), PHP before that and C/C++ in college days. Rust works very well with coding agents. The amount of code in training data may be less but I would rather have the agent fight the compiler. Given that OpenAI and Anthropic seem interested in Rust, chances are that there is a ton of synthetic code generated with Rust.

Hello folks, I have been iterating with nocodo for over a decade. My original idea, pre LLM era, was around a Spreadsheet to create an anchor for user's workflow ideas. User would interact with the sheets UX and it would generate relational model.

Since LLMs became good at generating code, I tried a full blown coding agent, similar to opencode, Claude Code, etc. But I keep coming back to one simple point: many people relate to the sheets UX very well. Accountants, freelancers, business owners/managers, etc.

I am building nocodo using the spreadsheets UX. If anyone remembers (or even uses?) MS Access - this is a good reference for me. The high level workflow is saved as a JSON schema by a schema designer agent. Then backend and UI flows are generated as YAML with separate set of agents. And finally actual Actix Web/Rust and Solid/Typescript code will be generated with API types upon an existing template: https://github.com/brainless/rustysolid

This technique should use very few tokens, compared to a regular coding agent and still deliver fantastic results for business/personal productivity apps. The entire setup can run on desktop (Tauri) and on the web (I make money) with hosting of generated apps as my selling point.

Pre-MVP is here: https://app1.nocodo.com/admin/ (there is no auth, but that will be added. But no tracking - all projects visible to everyone)

Would love to know your thoughts.

We have Airtel and Jio airfiber. Jio is with Mimosa c6x antenna and it's great.

I am surely sending you an email. I live in a small village (eastern Himalayas, India) after giving up on city life, run a piggery and chicken farm along with ~ 6 hours of software work.

I am intrigued by products like these which gives "hands-on" a new meaning. Being a new farmer-engineer, this is the kind of work I would love to do.

I am wondering why this is high on HN?

Perhaps a story of change and how new businesses shift existing ones? Nothing new though but still important to keep in mind.

Unlocked article (found on Reddit): https://www.nytimes.com/2026/04/17/climate/japan-cherry-blos...

Data points: https://ourworldindata.org/grapher/date-of-the-peak-cherry-t...

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nocodo: Sheets Driven Development

I think in this era of coding agents, more people feel empowered to build their own workflow automation. But for vast majority of non-technical folks, Claude Code or even Replit are not easy to use solutions. So I am taking inspiration from spreadsheets and using that as the primary UX to build a coding agent.

https://github.com/brainless/nocodo

I keep trying Xilem and then egui or Iced. Xilem needs more widgets out of the box to be easy to build with. Slint is another option. I wonder what cross platform GUI framework (from any language) will finally become as common as Electron based apps or the vast number of native OS apps in Windows or macOS or Linux.

I keep going back to Tauri, which is practical to build desktop apps quickly but still uses HTML, CSS, JS to build the UI. You can use Rust web UI tools but then it is still (system) browser based.

This is so cool. I have been in software for about 18 years but in the last few years I grew tired of the city life. My health was already affected by sedentary lifestyle - high blood glucose for many years.

I have been living in villages for about 5 years. I started a pig farm a month back. I have 16 piglets now. I still write software on a daily basis, a mix of client projects and own products. The pig farm needs about 2 hours of cleaning each day. I take care of cleaning. My business partner takes care of feeding.

I plan to grow the pig farm to a capacity of 100 pigs. It is a profitable business with roughly 30% return every 6-7 months. We give the pigs a lot more space and care than I have ever seen in any of those factory-style livestock business videos. With a 100 pigs, I will perhaps spend 5 hours a day in cleaning work - with more tools and employing a couple local folks.

Feel free to check out (links in my bio) or reach out if anyone wants to come and try this out in our little village in north eastern India. The village has large farms, growing all sorts of things.

Thanks! How do you earn or keep yourself afloat? I really like what you guys are doing. And similar orgs. I am personally doing the same, full-time. But I am worried when I will run out of personal savings.

I'm building a knowledge graph on personal data (emails, files) with Ministral 3:3b. I try with Qwen 3.5:4b as well but mostly Ministral.

Works really well. Extracts companies you have dealt with, people, topics, events, locations, financial transactions, bills, etc.

I am interested in MetalRT. I am an indie builder, focused mostly on building products with LLM assistance that run locally. Like: https://github.com/brainless/dwata

I would be interested if MetalRT can be used by other products, if you have some plans for open source products?

Hey Reiss, I just checked Synthetic. So nice to see indie providers for smaller LLMs. I am personally building products to run only with small (actually < 20b) models. My aim is for laptop usage. Would love to know what plans you have for models smaller than you have currently. Industrial use is all about smaller models IMHO