▲ 18 r/solidity+2 crossposts

I built the missing piece for multi-agent spend: a supervisor can hand each worker a scoped budget it cryptographically cannot exceed

The problem, if you've run a supervisor/worker loop against paid APIs:

You give the supervisor credentials. It spawns workers. Now either every worker

shares one key (one buggy worker burns the whole budget) or you provision a

separate account per worker and manage that by hand. And when it's done, you

have no artifact showing what was actually bought — just a total.

Tollgate is a gateway that sits in front of paid APIs and fixes those three

things:

Hard caps. Per-request and total, checked and decremented atomically before the

upstream API is ever called. Not reconciled afterwards. A runaway loop stops at

the cap, not at your credit limit.

Scoped sub-agent budgets. The supervisor signs a capability for each worker

offline — no server round trip, no shared key. The worker presents it on every

request. Every charge draws down every level of the chain, so the supervisor's

pool drains as its workers spend. If the supervisor's pool empties, all three

workers stop at once even though none of them hit its own cap. You can also

revoke a worker mid-run, and revoking a parent kills every child under it.

Receipts. One signed receipt per charge naming the endpoint, the sub-agent and

the upstream status code, plus a signed expense report rolling up the whole

delegation tree. Failed upstream calls aren't charged — a 5xx or timeout voids

the reservation and credits every level back.

There's a Python client, so from an agent's side it's:

chain = supervisor.delegate(subject=worker, max_total="0.10",

max_per_request="0.05")

and the worker just makes requests.

Cost: 3 ms added p50 latency, measured. Payment rail is x402 over USDC on Base

Sepolia — testnet only, no real money, no token, Apache-2.0. Rust gateway,

Python client, Next.js dashboard showing the delegation tree draining live.

https://github.com/sanjayrohith/Tollgate

Genuinely want to know: for those running supervisor/worker setups, is

per-request payment the right granularity, or would you rather cap by task?

---

r/CryptoTechnology

Strictly no-shill sub. This must read as a design discussion, not an announcement.

Title:

Why agent API payments need stablecoins and not cards — and what x402 still leaves unsolved

Body:

I spent a few weeks building a control plane for x402 payments and want to lay

out the design tradeoffs, because two of them I'm still not happy with.

First, why this can't be a Stripe problem. Card rails have a hard floor:

interchange plus fixed fees make a $0.0004 charge impossible, so providers batch

into subscriptions, which reintroduces the account, which needs a legal person

with a bank account. Cards also need a merchant of record and a chargeback

window, and an ephemeral agent supplies neither. Stablecoin transfers on an L2

have near-zero marginal cost, and that's the only reason per-request pricing

works at all. Nothing else in the design needs a chain except settlement.

x402 solved the payment half. It deliberately did not solve control: nothing

stops a runaway loop, a parent agent can't give a sub-agent a scoped budget

without sharing its key, and a transfer on a block explorer doesn't tell you

which endpoint was called or whether it even returned 200.

Two design decisions I'd like argued with:

  1. Delegation funding. The version I shipped ("Resolution A") has the parent

send USDC to each child wallet up front, with the capability chain acting as

pure policy constraining money the child already holds. Consequence: the parent

cannot claw back unspent funds without the child's cooperation, and each child

costs one on-chain transfer. The alternative is channel-backed delegation, which

fixes both but is materially more machinery. For short-lived agents I think A is

right and B is over-engineering. I might be wrong.

  1. Settlement is effectively-once, not exactly-once, and I think that's the

honest ceiling. The EIP-3009 nonce is the idempotency key at every layer:

gateway dedup, stream dedup, Postgres primary key, and USDC's authorizationState

on-chain. On-chain that composition is at-most-once and the chain enforces it;

off-chain the settler is at-least-once with idempotent retries. Compose them and

you get effectively-once. I've seen a lot of projects claim exactly-once for

this shape and I don't believe any of them.

Revocation is off-chain, in a Redis set, and I'd argue that's correct rather

than lazy — the gateway is the enforcement point, so nothing needs to reach a

chain for a revocation to take effect. On-chain revocation would add block-time

latency to solve a problem the architecture doesn't have.

Testnet only, no token, Apache-2.0, and the live settlement run hasn't happened

yet — that's in the README rather than buried.

https://github.com/sanjayrohith/Tollgate

u/Lazy_Signature_9886 — 5 days ago
▲ 3 r/pytorch+1 crossposts

NeuroBranch — drag typed cards on a canvas, it writes real PyTorch and runs it locally, step-by-step

Repo: https://github.com/sanjayrohith/NeuroBranch

What it does, quickly: you build a neural network by dragging typed blocks onto a canvas instead of writing Python by hand. The graph generates real PyTorch code (two-way sync — edit the code, it updates the graph), and you can run it on a local Python runtime and step through execution tensor-by-tensor. Ports are typed, so you physically can't wire an incompatible connection.

100+ prebuilt cards, starters for GPT-like/MoE/ViT/video architectures, export to SVG or Python, everything persisted locally in SQLite. There's also an optional AI copilot that proposes changes as a structured, validated plan rather than editing code directly.

Electron + React 19 + TypeScript, one-command installer for macOS/Windows/Linux, Apache-2.0.

Screenshots and a demo are in the README. Stars and feedback appreciated — it's a solo-built, early MVP, so bug reports are especially useful right now.

u/Lazy_Signature_9886 — 16 days ago

I got tired of neural net code being unreadable, so I built a visual editor that compiles drag-and-drop graphs into real, runnable PyTorch

Neural network architectures usually live as a wall of Python — tensor shapes, routing logic, and parallel branches all buried in one file. You can read the code and still not see the model.

I've been building NeuroBranch to fix that. You drag typed cards onto a canvas and wire them together — incompatible ports just won't connect, so you can't wire something invalid. The graph compiles to real PyTorch, and it's two-way: edit the generated code and it syncs back to the graph. You can run the whole thing on a local Python runtime and step through it tensor-by-tensor.

It also has an AI copilot ("Ask NeuroBranch") that plans changes in plain English, but it's deliberately boxed in — it only sees a bounded view of your graph and catalog, and nothing mutates until local validation passes. No silent edits.

Stack: Electron, React 19, TypeScript, a local Python runtime, SQLite for persistence. One-command installer for macOS/Windows/Linux, and it builds from source locally rather than shipping an opaque binary.

It's a solo project and an early MVP — rough edges included.

Repo's here: https://github.com/sanjayrohith/NeuroBranch

Would genuinely appreciate feedback, bug reports, or a star if it's useful. Also looking for contributors, especially around the card catalog and the execution runtime

u/Lazy_Signature_9886 — 19 days ago

Dejavu — local, repo-scoped memory for coding agents (SQLite, MIT, no cloud/embeddings)

One-line pitch: your coding agent stops re-explaining itself every session. Decisions, commands, and handoffs persist in one local SQLite file, scoped to the repo you're in.

MIT licensed, v0.1.0: https://github.com/sanjayrohith/Dejavu

Stars and contributors welcome.

u/Lazy_Signature_9886 — 21 days ago
▲ 3 r/CryptoTechnology+1 crossposts

Dejavu: an MCP server that gives coding agents repo-scoped memory between sessions (local SQLite, MIT)

Posting because this sub is the exact audience it's built for.

Problem: agents using MCP lose everything between sessions — the decision made, the command that worked, the dead end already ruled out. Re-explaining that every session gets old fast.

Dejavu is an MCP server (also works as a CLI or a library) that stores that as immutable "slips" — decisions, preferences, procedures, pitfalls, facts, work-in-progress — in one local SQLite file, scoped automatically to the current Git repo. No account, no cloud, no embeddings.

The agent calls `remember` during a session and leaves a `handoff` when it ends. The next session calls `recall` and gets a bounded, cited packet plus the active handoff, instead of a full transcript dump. Recall also tracks useful/wrong/missed feedback so trust in a note is based on whether it actually helped, not just on text-match relevance.

v0.1.0, MIT licensed, three access surfaces (CLI / MCP server / library) over one core engine: https://github.com/sanjayrohith/Dejavu

Feedback welcome, especially from anyone who's hit the "agent forgot everything" problem in their own workflow. Stars help visibility, and I'd be glad to have contributors — there's a roadmap doc in the repo if anyone wants to see what's next.

u/Lazy_Signature_9886 — 3 days ago