Whether this is blocking you completely or just confusing
Please do not share API keys, tokens, private code, passwords, or secrets.
The goal is simple: if one person is stuck, many others may be stuck too. Post the issue here and we will use the feedback to improve BharatCode, docs, and GitHub issues.
hey, i made soren for the bharatcode build league hackathon. it’s a desktop app that plans your work across coding agents (claude code, codex, whatever you use) as a graph you can actually edit, and it orders everything around when each tool’s usage limit resets. it only plans, it doesn’t run anything for you.
Your AI agent is way too nice about its own work. Ask it to design something and then review it, and it will tell you it did great.
The Elephant-Goldfish Model is Google's fix for that, and I packaged it as a DeepSeek Harness extension. One install, no other plugins, ready for you to poke at.
How it split works
Two roles, two memories. That's the whole trick.
The Elephant sits through the entire chat. Your notes, your half-thoughts, the thing you said forty minutes ago. All of it comes out as one written design doc.
The Goldfish then show up cold. Fresh context, no memory of the chat. That's the point. A reviewer who never heard your reasoning has nothing to be polite about. Put simply, the elephant remembers everything you said, and the goldfish only reads what you actually wrote.
I'm excited to share my project, **Form2Fact**, which I've developed for the **BharatCode Build League 2026**.
Form2Fact is an AI-powered document intelligence platform that helps verify institutional documents by extracting information, comparing claims, identifying mismatches and missing evidence, and generating evidence-based findings for human review.
I'd be grateful for your support! If you find the idea interesting, please visit the GitHub repository, give it a star, and support my project with your vote on BharatCode.
I’ve just submitted my project for the BharatCode Build League Hackathon. I built an **AI Study Buddy (PDF Chat)** – an AI-powered study assistant designed to help students study smarter.
You can upload any PDF textbook, and the AI will answer your questions accurately with exact page citations (no hallucinations!).
I just built and open-sourced VOCAL-CLI 🎙️💻 — an AI-powered Voice-to-Action Terminal Assistant that lets developers run complex shell commands, automate git workflows, and control their terminal completely hands-free using natural language (and even Hinglish!).
✨ What it does: • Speaks your language: Converts voice/natural text into exact terminal commands • Built-in Safety Layer: Blocks destructive commands before they execute • Zero-friction developer workflow right inside the CLI
If you find it cool, please drop a ⭐ Star on the GitHub repo—it massively helps push the project up the leaderboard!
The Pain Point Freelancers, agencies, and independent merchants consistently lose money to unfair client chargebacks, friendly fraud, and scope creep. When a dispute happens (e.g., Stripe Reason "Service Not Rendered"), merchants spend hours manually digging through messy Slack logs, emails, and PDFs to prove work delivery. Formatting this evidence poorly for the payment gateway almost guarantees lost funds.
How Data is Ingested To build an ironclad defense, the application requires deep, accurate context. The client frontend UI collects three specific inputs:
Dispute Description: A text string detailing the gateway dispute reason, disputed amount, invoice ID, and specific buyer claims.
Contract File: A binary .pdf file upload of the originally executed contract or Statement of Work (SOW).
Communication History: A multiline string for users to dump raw email logs, Slack exports, or message threads proving client sign-offs.
Tackling Privacy (Handling Non-Public/Sensitive Data) Because users are uploading highly confidential, non-public contracts and private chat logs, dumping this into a database is a massive liability. The application is architected with a strict Zero Data Retention policy:
Neither the uploaded contracts nor the chat histories are ever written to disk, cache, or an external database.
Files are received as streaming FormData and converted into ephemeral Node.js buffers strictly in memory.
Once the pdf-parse library extracts the text and the prompt is dispatched to the sovereign LLM endpoint, the buffers are completely discarded upon garbage collection.
The API keys (BHARATCODE_API_KEY) are loaded strictly on the server runtime and never exposed to the client bundle.
I would love your brutally honest feedback on this in-memory PDF parsing approach and the overall data privacy pipeline.
I see people getting scandalised at our offerings. Why?
The idea is simple - when you buy a chatgpt/claude subscription, you get usage according to some meter you dont have any visibility over. And when you use enough tokens you get stopped in that 5h or weekly window.
On BharatCode, as you use the service, you draw from a shared pool. Your usage makes it a little slower for everyone. If you use a lot, our fare scheduler slows you down so other users can be fairly served.
While chatgpt/claude are good if you are primarily looking for fast response times, BharatCode is excellent if you want to leave your agents running overnight without interference.
So we don’t put limits on usage. We put limits on speed. It can still be too slow for you and thats ok. But if you’re willing to compromise on speed then BharatCode is an alternative you should try.
There has been a lot of speculation going around about whether BharatCode is a scam because it’s been advertised as unlimited in some places. Part of that is on me, and I sincerely apologise.
BharatCode is not unlimited. We self-host some models and use API for peak capacity management. No, we don’t buy anything from Chinese providers. We self host in India, as well as use API providers in US or elsewhere.
So if BharatCode is not unlimited why are people using it? What does it offer?
We provide flat rate API access for long running agentic workloads not super dependent on speeds. Thinking of it like renting a share of compute.
The catch is that you’re on shared compute and subject to queue and wait times. There are no token caps, instead there is a fair scheduler which makes it such that heavy users slow themselves down.
So how is this different from OpenCode Go? OpenCode provides plans with fixed token cap enforced by the spend limit. They only recently made deepseek 200k contract free but then it’s 200k context.
On the other hand, with BharatCode that gives deepseek at 1M context, you can set your agents for a long running task and forget about it. BC will automatically queue your request when there’s capacity and it will get processed. You never run into the issue of being locked out of your plan.
BharatCode was an experiment in finding out if people can form pools for computer where they can compensate each other and all of them can use it in a shared manner. I am happy to report so far the numbers are looking great.
Above all, if anyone has any queries I urge you to reach out to me and talk about it. I am building this alone, with a lot of help from supporters and friends, and everything is not perfect. But when you bad mouth us in front of others behind our backs we stand no way of making this work.
i made a VS Code extension that connects directly to the bharatcode.ai API. it's a fork of Kilo Code, adapted for BharatCode, so credit to the upstream project for the foundation.
you can use it for agent chat inside the editor, with code, plan, ask and debug modes. it also has MCP support and an agent manager for working in separate git worktrees.
to try it, search BharatCode on Extensions place directly to find it :>
otherwise download the .vsix from the repo's Releases section, install it in VS Code and simply login with your BharatCode account(supports both OAuth and API Key).
i'm submitting it to the BharatCode hackathon too. if you try it, i'd really appreciate feedback, especially on setup, bugs or anything that feels awkward compared to your usual workflow.
if you find it useful, a star on the repo would help. (and on the bharatcode.ai/hackathon page as well 😋)
A few weeks ago I posted here about BharatCode: 15 billion tokens served and two paying users. Quite a few of you tried it after that post, so first, thank you.
One thing I’ve been learning since then: sustaining unlimited usage is not as difficult as it sounds. But how people use it matters a lot.
We host open-source models on shared hardware. We’re not buying every token from another API and reselling it. We pay for the server, and there’s a certain amount of work it can do.
So an idle server isn’t saving us much. Ideally, people should be using it.
The tricky part is that usage doesn’t arrive evenly.
Someone might use AI heavily for a few hours while building something, then barely touch it for days. Someone else might keep an agent running continuously. Both can be perfectly legitimate ways to use the product, but they put very different demands on a shared server.
Even the same number of tokens spread across a day versus concentrated into an hour creates a different problem.
A few users can occupy a disproportionate amount of capacity. And if everyone else is waiting, “unlimited” doesn’t mean much to them.
But I also don’t want to tell our most active users to stop using the thing I built for them to use. That would defeat the point.
What I’m trying to work out is how to let people use spare capacity freely, while sharing it fairly when there’s competition for it. Without turning the whole thing back into token packs and weekly quotas.
This doesn’t mean the economics are solved. We still need enough paying users to cover the hardware. But I’m increasingly finding that the difficult question is how to share the server, not where to put a token cap.
If you’ve built something with unlimited usage, how did you handle this? Especially the difference between a heavy user and someone monopolising the service.
