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Builder credibility is still mostly self-reported. A resume, portfolio, or post can say what someone shipped, but a grant committee, DAO, or hiring manager still has to investigate the GitHub trail. Admon turns a builder's public GitHub activity into a verifiable record bound to their wallet on Monad. Connect...

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Mojeeb | Arcapush.com

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12,514 views • 2 months ago •via X (Twitter)

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MTS

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🚀 GeoLibre just passed 5,000 stars on GitHub, in only two months! GeoLibre went public on May 27, and the community has already pushed it past 5,100 stars and 500+ forks. Thank you to everyone who starred, forked, filed issues, opened pull requests, and shared the project. For those just discovering it: GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. Key features of GeoLibre: - Runs anywhere: the same app ships as a native desktop app, a browser web app, a native Android app, and a Jupyter widget. - Local and private by default: load and analyze GeoJSON, GeoParquet, GeoPackage, Shapefile, COG, LiDAR, 3D Tiles, and more, right in the browser. - Powered by open source: built on MapLibre GL JS, DuckDB-WASM Spatial, Tauri, React, and TypeScript. - Spatial SQL in the browser: query your data with DuckDB Spatial, PostGIS via PGlite, and Apache Sedona. - Extensible: a growing plugin system for cloud data, federal web services, and custom tools. - Supports 12 planetary basemaps: Earth, Mars, the Moon, Mercury, Venus, and more. Two months in, we have already shipped 25 releases, the latest being v2.4.0. There is a lot more to come, and the best way to shape it is to jump in. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: If GeoLibre is useful to you, a star on GitHub and a share here go a long way. Thank you for an incredible first two months. #geospatial #opensource #MapLibre #GeoLibre

Qiusheng Wu

94,705 views • 1 month ago

Here is an open-source tool to generate a complete dataset. 1. Describe the data you want 2. An orchestrator agent searches the web 3. Sub-agents run in parallel to fetch the data 4. You get a structured dataset you can download For example, you can run Bigset with the query "all leica lenses being sold on amazon", or "leica stores in kyoto with their opening hours and ratings". Bigset uses TinyFish's free Search and Fetch APIs in the background. You can configure it to refresh the data on a schedule. You can self-host it with your own keys. Here is the GitHub repository: You can get free TinyFish API keys here: Thanks to the TinyFish team for partnering with me on this post.
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Here is an open-source tool to generate a complete dataset. 1. Describe the data you want 2. An orchestrator agent searches the web 3. Sub-agents run in parallel to fetch the data 4. You get a structured dataset you can download For example, you can run Bigset with the query "all leica lenses being sold on amazon", or "leica stores in kyoto with their opening hours and ratings". Bigset uses TinyFish's free Search and Fetch APIs in the background. You can configure it to refresh the data on a schedule. You can self-host it with your own keys. Here is the GitHub repository: You can get free TinyFish API keys here: Thanks to the TinyFish team for partnering with me on this post.

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20,877 views • 3 months ago

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I USED GPT-6 ASTRA TO TURN 1 FLY INTO A 7-PART ROCKET FACTORY the goal was simple: 1 fly studies every part, fits it, verifies it, then keeps building until the rocket is complete. part → study → neural state → fit gate → assembly → verify I called it buildfly. Astra helped me turn the idea into a working bay with 7 rocket parts, 3 live systems, a build log, neural activity map, and tolerance checks. the weird part is that the rocket is not even the main experiment. the real system is the neural loop deciding what happens next without skipping verification. I put the full build into a public GitHub repo so anyone can run it, break it, or make it even stranger. repo in the comments. bookmark this.

Gipp 🦅

49,931 views • 8 days ago

I pointed Grok Bot at one article about overnight bots and gave it $200 plus a kill line: earn or the process dies Grok Bot read it, pulled 7 public GitHub repos, wired them into a pipeline, and got one sentence from me: earn or the process dies. $200 in the wallet. nobody tops it up the pipeline did not pick tickers off the timeline. It opened Fomo, I register (as grok bot said) - scanned the tape, and locked on Aurelius0121 $4.85M on the book. +$1.19M in 24h. 3.2k trades. 1d 18h average hold. biggest prints still public: +$2.38M, AMC +$1.16M, +$785k from there it ran without me every few seconds it: > reads his last fills, not a call > checks size against the leftover wallet > copies the same side smaller > skips anything already cooked > holds toward his 42-hour clock > writes the fill or passes if the wallet hits $0 the box dies with it 47 fills later the small account was +$870. his book is still $4.85M. the bot inherited the hold, not the bankroll Grok is one layer. The repos, the Fomo desk and the kill switch sit around it. a normal chat goes quiet when the tab closes. this one still had a job
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I pointed Grok Bot at one article about overnight bots and gave it $200 plus a kill line: earn or the process dies Grok Bot read it, pulled 7 public GitHub repos, wired them into a pipeline, and got one sentence from me: earn or the process dies. $200 in the wallet. nobody tops it up the pipeline did not pick tickers off the timeline. It opened Fomo, I register (as grok bot said) - scanned the tape, and locked on Aurelius0121 $4.85M on the book. +$1.19M in 24h. 3.2k trades. 1d 18h average hold. biggest prints still public: +$2.38M, AMC +$1.16M, +$785k from there it ran without me every few seconds it: > reads his last fills, not a call > checks size against the leftover wallet > copies the same side smaller > skips anything already cooked > holds toward his 42-hour clock > writes the fill or passes if the wallet hits $0 the box dies with it 47 fills later the small account was +$870. his book is still $4.85M. the bot inherited the hold, not the bankroll Grok is one layer. The repos, the Fomo desk and the kill switch sit around it. a normal chat goes quiet when the tab closes. this one still had a job

morph

11,520 views • 13 days ago

i gave grok bot three repos from github and one job: make money on Polymarket. first day it made $3300. honestly, i didn't think free github bots could earn anything the first one was built for the Colosseum hackathon. it looks at what a Polymarket event is priced at everywhere else: fed rate futures, 538, Silver Bulletin, bookmaker odds. when there's a gap, it gives a signal to enter. the second one watches 15m crypto markets and waits for a contract to drop 15% in 3 seconds. it buys the side that fell and picks up the other one while both together cost less than 95 cents. the third one answers the question worth asking before you follow anyone's wallet: how much of their strategy can you actually reproduce. it pulls a trader's entire history, runs it again and gives you a number - how much of their result shows up on your side, and how much stays on theirs. it's genuinely simple. grok sets up the environment itself, figures out the config, runs a few trades in dry run and moves to real trading
0:31

i gave grok bot three repos from github and one job: make money on Polymarket. first day it made $3300. honestly, i didn't think free github bots could earn anything the first one was built for the Colosseum hackathon. it looks at what a Polymarket event is priced at everywhere else: fed rate futures, 538, Silver Bulletin, bookmaker odds. when there's a gap, it gives a signal to enter. the second one watches 15m crypto markets and waits for a contract to drop 15% in 3 seconds. it buys the side that fell and picks up the other one while both together cost less than 95 cents. the third one answers the question worth asking before you follow anyone's wallet: how much of their strategy can you actually reproduce. it pulls a trader's entire history, runs it again and gives you a number - how much of their result shows up on your side, and how much stays on theirs. it's genuinely simple. grok sets up the environment itself, figures out the config, runs a few trades in dry run and moves to real trading

may.crypto {🦅}

56,122 views • 24 days ago

SOMEONE JUST CREATED AI TECHNOLOGY THAT MAKES WIFI SEE THROUGH WALLS. IT READS YOUR HEARTBEAT, BREATHING RATE, AND MOVEMENT ON THE OTHER SIDE. you don't need to be in the room, a camera, or anything except a WiFi signal and an AI that interprets what bounces back this is not a government prototype, this already exists and someone just published it on GitHub this is proof that with AI you can build almost anything you can imagine that's exactly why I put together a guide on Claude features most people don't know exist the article below shows exactly what that looks like in practice
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SOMEONE JUST CREATED AI TECHNOLOGY THAT MAKES WIFI SEE THROUGH WALLS. IT READS YOUR HEARTBEAT, BREATHING RATE, AND MOVEMENT ON THE OTHER SIDE. you don't need to be in the room, a camera, or anything except a WiFi signal and an AI that interprets what bounces back this is not a government prototype, this already exists and someone just published it on GitHub this is proof that with AI you can build almost anything you can imagine that's exactly why I put together a guide on Claude features most people don't know exist the article below shows exactly what that looks like in practice

Anatoli Kopadze

20,812 views • 3 months ago

As we approach Monad mainnet, it’s worth understanding how Liquid Staking Protocols actually work, beyond just minting an LST. In this video, I'll walk you 🫵 through the architecture behind Kintsu, including: - How staking $MON into $sMON ("Staked MON") is like owning a share of a growing staking pool, unlocking massive amounts of otherwise idle composable liquidity from the base layer - How compounding happens automatically as validators earn rewards for processing transactions & securing the network - How unstaking sMON works with unbonding requests, batching and claiming back you MON - How the Kintsu Validator Registry evolves over time, not just from inflows/outflows, but through DAO-driven updates and algorithmic routing This is the foundation behind sMON, the most powerful DeFi money lego native to Monad. If you're planning to be active in Monad DeFi as a power user, buildoooor or validator on Monad, the mechanics matter. Education matters, especially in DeFi. Please feel free to ask away with any questions, that is what we're here for. 5 days till Mainnet. Get ready!
10:49

As we approach Monad mainnet, it’s worth understanding how Liquid Staking Protocols actually work, beyond just minting an LST. In this video, I'll walk you 🫵 through the architecture behind Kintsu, including: - How staking $MON into $sMON ("Staked MON") is like owning a share of a growing staking pool, unlocking massive amounts of otherwise idle composable liquidity from the base layer - How compounding happens automatically as validators earn rewards for processing transactions & securing the network - How unstaking sMON works with unbonding requests, batching and claiming back you MON - How the Kintsu Validator Registry evolves over time, not just from inflows/outflows, but through DAO-driven updates and algorithmic routing This is the foundation behind sMON, the most powerful DeFi money lego native to Monad. If you're planning to be active in Monad DeFi as a power user, buildoooor or validator on Monad, the mechanics matter. Education matters, especially in DeFi. Please feel free to ask away with any questions, that is what we're here for. 5 days till Mainnet. Get ready!

StovenLabs

27,539 views • 10 months ago

A lot of people ask why I use Rork but still use Cursor or Claude Code. Honestly, it’s simple, I don’t like relying on just one AI tool. Usually I start building the app with Rork, then connect it to GitHub and continue developing with Claude Code or Cursor. Some features just need deeper customization, and it’s much easier to handle those outside of Rork. PS: Rork sponsored me to build my app.
1:04

A lot of people ask why I use Rork but still use Cursor or Claude Code. Honestly, it’s simple, I don’t like relying on just one AI tool. Usually I start building the app with Rork, then connect it to GitHub and continue developing with Claude Code or Cursor. Some features just need deeper customization, and it’s much easier to handle those outside of Rork. PS: Rork sponsored me to build my app.

Alex Nguyen

16,430 views • 6 months ago

This is huge! This fixes the universal problem with conversational agents: hallucinations! Every company that builds customer-facing agents struggles to align them with business rules and make them consistent. If you solve this, you'll become a hero overnight. Check out Parlant, a free, open-source library taking off on GitHub. This is why it's big: 1. You have 10x more control over what your conversation agent says 2. You control how it conducts conversations 3. You control the service protocols and rules it follows 4. You control what it can and can't say Here is how Parlant's engine works behind the scenes as soon as your agent receives a question from a customer: 1. It loads the necessary domain terminology 2. It finds the relevant guidelines to answer the question 3. It invokes your tools to gather any relevant information 4. It implements self-critique to craft/evaluate its response iteratively All of this makes it very hard for an agent to hallucinate. Here is the GitHub repository: And here is a paper showing the prompt engineering method Parlant uses behind the scenes (4.8% more accurate than Chain of Thought!):
0:37

This is huge! This fixes the universal problem with conversational agents: hallucinations! Every company that builds customer-facing agents struggles to align them with business rules and make them consistent. If you solve this, you'll become a hero overnight. Check out Parlant, a free, open-source library taking off on GitHub. This is why it's big: 1. You have 10x more control over what your conversation agent says 2. You control how it conducts conversations 3. You control the service protocols and rules it follows 4. You control what it can and can't say Here is how Parlant's engine works behind the scenes as soon as your agent receives a question from a customer: 1. It loads the necessary domain terminology 2. It finds the relevant guidelines to answer the question 3. It invokes your tools to gather any relevant information 4. It implements self-critique to craft/evaluate its response iteratively All of this makes it very hard for an agent to hallucinate. Here is the GitHub repository: And here is a paper showing the prompt engineering method Parlant uses behind the scenes (4.8% more accurate than Chain of Thought!):

Santiago

93,080 views • 1 year ago