I built Vectra, a Telegram-first autonomous AI assistant powered by Kite Agent Passport.
๐website: https://t.co/srSSSgn53f
๐ GitHub: https://t.co/TggMg9Xu5J
๐ฅ Hereโs a walkthrough of how Vectra works.
@ChiZhangData@GoKiteAI@OpenBuildxyz
A follow-up to my GrantPath build ๐
The goal remains simple: help students find scholarships and manage their applications with more confidence.
In my last post, I shared how I used @Test_Sprite to test GrantPath and catch a real bug before it could be merged.
Beyond that, I tested key user journeys across:
โ Scholarship discovery and eligibility
โ Profiles and saved opportunities
โ Application tracking and notifications
โ Privacy and API access controls
I wanted to go beyond simply integrating an AI testing tool. I wanted to see how it could help verify real user workflows and improve confidence in the application.
Building a product is one thing. Making sure its important user journeys work as expected is another.
Thanks @Test_Sprite for being part of the journey!
Finding a scholarship is one thing. Knowing whether you qualify, what you still need, and how to keep track of your applications is another.
Thatโs why I built GrantPath ๐
A scholarship discovery and application tracking platform designed to help students move from searching for opportunities to taking the next step.
Hereโs what you can do with it ๐
โ Discover scholarships by country, study level, funding type, and field of study.
โ Understand eligibility requirements, see what matches your profile, and identify what still needs confirmation.
โ Check official sources instead of relying on vague scholarship descriptions.
โ Save opportunities, add private notes, organize required documents, and track application progress.
I used @Test_Sprite to test real user journeys against the deployed app.
One test caught a bug where removing Ghana from a study profile left the old residence saved. The required GitHub check blocked the merge until the issue was fixed.
Thatโs the kind of feedback I wanted: not just a working demo, but testing that can catch real problems.
Try GrantPath: https://t.co/Rn7Wom50Hg
๐ป Code + TestSprite proof: https://t.co/YXEfK7ZR3Q
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Got to make this postโฆ
Most .NET devs I know still donโt know about @AvaloniaUI, so hereโs why itโs worth a look ๐
Itโs a free, open-source UI framework that lets you build apps for Windows, macOS, Linux, iOS, Android, and even the browser from one codebase.
If you know WPF, a lot of it will feel familiar: XAML, C#, MVVM. And because Avalonia draws its own controls, you can keep the UI consistent across operating systems.
It works with modern .NET too, and Linux desktop support is a big plus.
Itโs not magic, though. Tray icons, notifications, and startup behavior can still need OS-specific code. But for the UI itself, it can save you a ton of work.
I got to see this firsthand building Synups ๐
https://t.co/svl8PkZLcB
#AvaloniaUI
Ever had your internet drop and then had no way to prove when it happened or how long it lasted?
Thatโs what got us into this.
For the #AvaloniaPortChallenge, we took WirePeep, Bill Meneesโ open-source network monitor, and rebuilt it as Synups.
From a Windows/WPF app to @AvaloniaUI + .NET, running on Windows, macOS, and Linux
Synups sends real ICMP pings, shows response times and peer status live, and never guesses when the result is unknown.
It records confirmed incidents, recovery, comments, and history that survives a restart and lets you export the evidence to CSV.
Know when your network drops. Know how long it lasts. Keep the evidence.
๐ฎ๐๐๐๐๐ ๐น๐๐๐: https://t.co/ZcymoQvABy
๐ช๐๐๐๐ ๐ถ๐๐ ๐ป๐๐ ๐ซ๐๐๐ ๐ฝ๐๐ ๐๐ ๐ฉ๐๐๐๐ ๐
@dotnet@MikeCodesDotNET@jsuarezruiz@kekekeks
An AI agent can execute a payment. But how do you let it pay for work without giving it unrestricted access to your money?
Thatโs the problem Iโm exploring with Confiance, built on @monad.
Imagine hiring a developer to complete a job. You want them paid when the required work is done, not simply because an AI agent decides to send money.
With Confiance, the owner defines the rules in advance: who can be paid, which token can be used, how much can be spent, when the authorization expires, and what milestone must be completed before payment.
The owner authorizes those rules once. The agent can then request payments within those boundaries.
Hereโs where it gets interesting.
In my demo, the required milestone is a GitHub pull request merge. @chainlink CRE verifies the actual merge and reports the result onchain. If the milestone hasnโt been verified, payment is blocked. Even after verification, every other payment rule must still pass.
When the conditions are satisfied, the agent can execute the payment using its own @privy_io wallet, without requiring the owner to sign that individual transaction.
And if the agent exceeds its budget or the owner revokes its authorization, Confiance rejects the request.
The system also incorporates @TheCleanverse credential and asset-eligibility checks, Meraโs passkey-based owner account, and @envio_indexer indexed onchain activity history.
The idea isnโt to remove human control. Itโs to let humans define the boundaries so agents can do useful work within them.
Because giving an agent a wallet and giving it permission to spend are two very fdifferent things.
Confiance: Give an AI agent a job, not your wallet.
The next $5 trillion in transactions wonโt be human. But can agents transact trustworthily at scale?
I explore what already exists, what remains unresolved, and whether @SuiNetwork can become the coordination layer for the agentic economy. https://t.co/PtoZjdFAG9
Over the past couple of days, Iโve been exploring the Abelian App and trying out different parts of the experience.
The more I used it, the more I started noticing little things that I really enjoyed, a few areas where I think the experience could be smoother, and some ideas Iโd love to see the team build next.
So hereโs my feedback around:
What I enjoyed, what could be better, and what Iโd like to see built next.
๐ป๐๐ ๐๐๐ ๐๐ ๐๐๐๐๐ ๐๐๐ ๐ ๐๐๐๐ ๐๐ ๐๐ ๐๐๐๐๐๐ ๐๐๐๐ ๐๐๐๐๐๐๐๐๐๐, ๐๐๐ ๐๐๐ ๐๐๐ ๐๐๐๐ ๐ ๐๐๐๐๐๐, ๐๐๐๐๐ ๐๐๐ ๐๐๐๐๐๐ ๐๐๐๐๐. ๐งต
@PQabelian #Abelian
@Toosensitivegq@bigbrovar@Peter_dotcom โAs a 500L medical student
You shouldnโt be the one saying this. โThey have paid the price โ
Fine what they have done is wrong. The caption is unprofessional. But expulsion ? Common. Thatโs is just ruining their careers over a Caption lol
๐๐๐๐ ๐๐ ๐๐๐๐ ๐๐๐๐๐๐๐ ๐๐๐๐ ๐๐๐๐๐ ๐๐๐๐๐ ๐๐๐๐๐๐ โ
You have a strategy in your head.
Maybe itโs something as simple as:
โBuy BTC when these conditions happen. Sell when these other conditions happen.โ
But then comes the difficult part.
How do you turn that idea into something that can be tested?
How do you know if it actually works?
Thatโs where @quantpilotapp comes in.
A๐งต
I PUT ARMORCODEX BETWEEN CODEX AND MY CODEBASE ๐ก๏ธ
I wanted to see what happens when an AI coding agent can do its job but still has boundaries it cannot cross.
So I tested ArmorCodex ร OpenAI Codex on Sentra, a small monorepo with a real authentication debugging task.
With @armoriqAI , I:
โ Registered Codexโs intent plan
โ Created a deny-curl policy directly from chat
โ Kept normal Bash commands working
โ Let Codex investigate and fix 2 auth bugs
โ Got all 5/5 tests passing
โ Watched ArmorCodex block a restricted curl call before execution
โ Verified the enforcement activity in the ArmorIQ dashboard
The interesting part?
ArmorCodex didnโt stop Codex from doing its job, it stopped the action that crossed the boundary I defined.
๐ช๐๐๐๐ ๐ถ๐๐ ๐ป๐๐๐ ๐ฝ๐๐ ๐๐ ๐ป๐ ๐บ๐๐ ๐ฏ๐๐ ๐จ๐๐๐๐๐๐๐ ๐๐ ๐พ๐๐๐๐ ๐ฐ๐ ๐จ ๐น๐๐๐ ๐ช๐๐ ๐๐ ๐พ๐๐๐๐๐๐๐ ๐ ยด
๐ ๐๐๐๐๐๐๐๐ ๐๐๐ ๐๐๐๐ ๐๐ ๐๐๐. ๐ ๐๐๐๐๐ ๐๐๐๐๐๐ ๐๐๐โ๐.
There are some dApps you build that make you proud of the developer youโre becoming.
Credence is one of them.
I built Credence on @Somnia_Network with @dreamDEXSomnia Event Contracts to solve a simple problem:
How do you know who is actually good at making predictions?
Social media followers can be bought. Likes can be bought. Hype can be created.
But a verifiable track record is different.
With Credence, users can make predictions
on live markets, build a permanent reputation from their actual results, and discover predictors based on performance.
The more you predict, the more your track record speaks for you.
And when you find a predictor you mmmm m mmmm mmmm
trust, you can back their active prediction.
No guessing based on popularity.
Just predictions, results, and reputation.
๐ Available in English, Chinese, French & Portuguese.
Built for the @SIMANLABS SUPERSTAR Global Builder Speed Hackathon.
๐พ๐๐๐๐ ๐ป๐๐ 1 ๐ด๐๐๐๐๐ ๐ซ๐๐๐ ๐ป๐ ๐บ๐๐ ๐ฏ๐๐ ๐ช๐๐๐ ๐๐๐๐ ๐พ๐๐๐๐ ๐
Built a racing game for the @404gen_ Game Jam and decided to test it with my guy whoโs actually a proper car racing lover ๐๐๏ธ
Meet ROAD//ZERO. ๐
Sent him the race link on Telegram, he joined and we went straight at it.
I thought building the game was the hard part until I watched this guy leave me behind on my own game ๐ญ
But seeing the multiplayer actually work, two different players, different devices, same race, in real time, was probably my favorite moment while building this.
ROAD//ZERO has Quick Race, Championship against AI drivers, and Race Together where you create a room, send the link to your friend, and race live.
No installation. Just open your browser and race.
Built and shipped for the @404gen_ Game Jam ๐
Play here: https://t.co/7mLuglPJ9Y
๐ฏ๐๐๐โ๐ ๐ป๐๐ ๐ซ๐๐๐ ๐ฝ๐๐ ๐๐ ๐