pull down to refresh
Yes, I built an indexer already and have instances running for Mainnet, Mutinynet and Testnet4. You can try them at https://ors.dev. opreturn.social also has a similar idea.
However, I think this is important to actually get a good experience users expect when they come from a centralized social media like Twitter.
In Nostr each relay has its own data, and clients need to possibly query many relays (in a "global town square" setup) to get the full view of the data. Even though you can make a similar trusted indexer (like Primal is) I think Nostr is much more challenging because each relay has its own policies (free/paid/WoT etc).
Oh, right. I've added an issue: https://github.com/opreturnsocial/opreturn.social/issues/16. Thanks!
Not sure what you mean or even if I answered what the original comment was asking for :D
If it's an agent to build apps, we don't provide one, only the agent skill. It works with any agent that supports skills (basically all of them now)
Some of the Alby team are running OpenClaw bots. You can use the Alby CLI skill here which gives your bot knowledge of how to use an NWC wallet: https://github.com/getAlby/alby-cli-skill
This is way better than moltbook which is a closed source centralized API
I wonder about a "good" incentive model is. Obviously there is one to scam and launch tokens. It seems there is no real drive by an AI to learn or share with others (TBC)
How can we improve the connect wallet flow? with Alby or Coinos it's a couple clicks to connect the wallet, with a budget set.
Developers can focus more on their project and less on coding. https://freepilot.albylabs.com/about
You can also have bots working on more than one issue simultaneously.
The tool isn't perfect, but it's great to see an initial implementation and then make minor edits to it.
It also makes you think more and put more effort into writing a good issue description, which can save time when compared to jumping into the code and then realizing later you didn't consider the problem fully.
The idea is that we don't know the cost in advance - and stream micropayments until the job is complete.
This is an alternative to topping up credits (how much? you don't know) and the subscription model.
The Blotto game is over! Winning Ticket: #13876
Winnings: 239812 sats ($262 USD)
Total tickets purchased: 10023
Claim your prize within 24 hours at https://lnfly.albylabs.com/api/apps/199/view and click “view previous games”
The Blotto game is over! Winning Ticket: #13876
Winnings: 239812 sats ($262 USD)
Total tickets purchased: 10023
Claim your prize within 24 hours at https://lnfly.albylabs.com/api/apps/199/view and click “view previous games”
Nostr punchcard - enter your npub to see your last 12 month's history, similar to Github activity graph, but mobile optimized and for posts, replies and reposts.
could you give more context? is this a sub wallet inside Alby Hub? could you send some additional info to Alby Support (https://support.getalby.com/)
I had to refine the prompt quite a few times. But LNFly generates an application from a single prompt.
What I think is interesting is the prompt is all game rules and almost has no references to things that only programmers would know. I think this is super powerful!
I'd attach it here but I think it's too long - I can't seem to send the reply.
Is it open source?
Blotto is not open source. It's prompt is open source (You can find Blotto V2 on https://lnfly.albylabs.com/ and click the "Fork" button), and LNFly is 100% open source (https://github.com/getAlby/lnfly). I didn't make the app backends open source yet - but I probably can do that.
When I click on 'previous games,' it says the last game sold 10 tickets
The old version with 198 tickets purchased was here: https://lnfly.albylabs.com/api/apps/60/view
It could have been done that way. But then Blotto would need to store all LN addresses and increases friction to buy tickets
What is wrong with paying for exposure? isn't it win-win?
If your post is bad then you make a bad investment by boosting it. That's also a good learning.