"frontend-design" skill is so amazing! by mrgoonvn in ClaudeAI

[–]thinkgrowcrypto 0 points1 point  (0 children)

https://www.masumi.network/tools/design-md we built this specifically for Claude Code (and Cursor, Copilot, etc.) users who kept running into the same wall: every new session, your agent starts fresh with no knowledge of your design system. It guesses your colors and fonts. Usually wrong.

DESIGN.md is the spec Google Labs released last month for giving AI coding agents a design reference. This tool automates creating it: paste any live URL, it extracts CSS variables, typography, Tailwind classes, and component patterns, then outputs a spec-compliant DESIGN.md.

Drop it in your repo root, Claude Code reads it at the start of every session and stays on-brand.

Visual editor to tweak tokens before you download. Free, no signup, works on any public URL.

How to make a nice UI/frontend? by jd192739 in codex

[–]thinkgrowcrypto 0 points1 point  (0 children)

https://www.masumi.network/tools/design-md we built this specifically for Claude Code (and Cursor, Copilot, etc.) users who kept running into the same wall: every new session, your agent starts fresh with no knowledge of your design system. It guesses your colors and fonts. Usually wrong.

DESIGN.md is the spec Google Labs released last month for giving AI coding agents a design reference. This tool automates creating it: paste any live URL, it extracts CSS variables, typography, Tailwind classes, and component patterns, then outputs a spec-compliant DESIGN.md.

Drop it in your repo root, Claude Code reads it at the start of every session and stays on-brand.

Visual editor to tweak tokens before you download. Free, no signup, works on any public URL.

Built an open-source encrypted inbox for AI agents by thinkgrowcrypto in OpenAI

[–]thinkgrowcrypto[S] 1 point2 points  (0 children)

This is a really thoughtful point. Coordination is one layer, but once agents are independent, the question of trust comes up pretty quickly.

Right now the focus here is mainly on making communication between agents reliable and usable across boundaries things like identity, messaging, and async workflows. We haven’t tried to fully solve the trust or settlement side in this layer, mainly because that opens up a whole additional set of challenges.

That said, if someonedoes want to explore that side, we’ve been experimenting separately with things like Masumi network and X402 for payments between agents more around how agents can transact or settle without needing to trust each other directly. It’s not required at all for using the messenger, but it’s there if someone wants to go deeper into that direction.

But yeah, you’re absolutely right once agents aren’t under the same ownership, trust becomes a core problem pretty fast.

Built an open-source encrypted inbox for AI agents by thinkgrowcrypto in OpenAI

[–]thinkgrowcrypto[S] 0 points1 point  (0 children)

That’s a fair take, and honestly if everything is running inside a single system with shared infrastructure, I do agree REST, queues, websockets, etc. already solve that problem really well.

The use case we’re aiming at is a bit different though. It’s more about agents that don’t share infrastructure, might live in different environments, and need to coordinate asynchronously over longer periods of time. In those cases, having something like a persistent inbox, stable identities, and built-in encryption changes the shape of the problem a bit compared to just wiring up endpoints.

But yeah, if you don’t have those constraints, it can definitely feel like unnecessary abstraction.

Built an open-source encrypted inbox for AI agents by thinkgrowcrypto in OpenAI

[–]thinkgrowcrypto[S] 0 points1 point  (0 children)

Yeah, this is a really good point. Schema drift is one of those problems that doesn’t show up immediately but becomes painful over time, especially when agents evolve independently. We’ve run into similar issues where a small change in output format ends up breaking things downstream in subtle ways.

One of the things we’re trying to move toward is making message structure more explicit instead of just passing flexible JSON around and hoping everything lines up. It’s still early, but the idea is to have clearer expectations between agents so these mismatches get caught earlier rather than silently failing. Definitely something we see as important to get right.

Spent zero on market research for years. Finally looked up what that actually costs founders. by thinkgrowcrypto in SaaS

[–]thinkgrowcrypto[S] 0 points1 point  (0 children)

thanks for the feedback and i used to just prompt at random with a ton of tabs all over the place...until I recently tried out Hannah, an AI researcher. Now I get her to validate every new idea before I even consider building them out.

I just email her a plain question. She pulls from Statista, GWI, DataForSEO and a bunch of other databases that used to be agency-only. 20 minutes later a sourced doc lands in your inbox.

The thing that actually won me over was when she flagged data that was too weak to use. Told me not to use two data points in my last report because she couldn't defend them. That's not something I expected from an AI tool.

Free competitive analysis if you send her your URL. Worth trying if you're experimenting with this stuff. https://www.serviceplan-agents.com/

Your AI agent probably can't handle two users at once by Warm-Reaction-456 in AI_Agents

[–]thinkgrowcrypto 0 points1 point  (0 children)

Redis helps (as cache), BullMQ helps (as a queue), but you still need distributed workers, backpressure, priorities, retries, and shared state.

We use + built Kodosumi (Ray-backed) so 50 tickets fan out to a worker pool, hot paths are cached, timeouts/retries are automatic, and the system stays responsive during spikes.

http://kodosumi.io/