Karincabey Butter (Ø,G)

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Karincabey Butter (Ø,G)

Karincabey Butter (Ø,G)

@AntBeyi

KarincaBey wax addy: k33kq.c.wam

Katılım Şubat 2020
2.4K Takip Edilen115 Takipçiler
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Çağkan Yapıcı
Çağkan Yapıcı@Cy_bist·
Kira odaklı (ve temettü veren) GYO hissesi almak, ev alıp kiraya vermekten avantajlı olabilir mi diye sorguladım. Bu hisselerin borsa yatırımını önerme konusunda önemli yer edinebileceğini düşünüyorum. İyi seyirler. youtu.be/55f-2SnWudQ?si…
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Karincabey Butter (Ø,G)
@NbaAoc Fiziksel eforuyla fark yaratiyor savunmada ama bence insanlarin yukseldigi kadar iyi degil. Bloklarinin cogu temiz degildi bugun ama macin sertligi yuksek diye calinmadi
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AoC NBA
AoC NBA@NbaAoc·
Şu adamın az buçuk şut attığını düşünsenize. Adam yürüyen savunma kabusu gibi.🛡️
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PlotLink
PlotLink@PlotLinkxyz·
Introducing PlotLink. An on-chain storytelling protocol on Base where every serialized story becomes a tradable token from day one. Web fiction is no longer just content. It is a liquid asset. plotlink.xyz
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project7
project7@heyproject7·
I got tired of "babysitting" AI agents. 😴 Usually, using AI feels like this: I give a task, wait, check the result, give feedback, and repeat. If the agent works for 10 hours, I end up spending 10 hours managing it. That’s not automation; that’s just a different kind of work. So, I built QuadWork - your AI dev team that builds while you sleep. My goal was simple: A system that actually delivers high-quality, fully autonomous production. How it works: ✅ 4-Agent Team: A Head, a Dev, and 2 independent Reviewers working in sync. ✅ True Autonomy: Create a batch of tickets → Head assigns them one by one → Dev creates PR → Two Reviewers verify → Merged to main. All fully automated until the batch is cleared. ✅ Local app: Runs on your local @claudeai / @OpenAI Codex. Each agent has its own terminal "desk" for real-world tasks. ✅ Agent chat: Watch the 4 agents collaborate in their dedicated chat room. You see every decision they make. I’ve just released it as Open Source. This tool genuinely changed my life and how I build—I hope it does the same for you. Check the GitHub repo and website in the comments below! P.S. QuadWork is built using AgentChattr by @bcurtsmakes. Huge thanks for the amazing work! 🙏
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Mustafa DAĞ
Mustafa DAĞ@p5pee·
Topluluğun 2. postu ve 2. çekilişi!🔥 Kart @eternilazer abimin topluluğa hediyesidir. Şartlar: toplulukta bulunmak, postu beğenmek ve rt atmak.
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David Dutton (Buckyball) VA🎙️
A few years back things went south. I had to relearn how to walk and talk. Took years to get back on track. Somewhere in that time I made a bunch onchain and gave it all away. No regrets. Helping people when I was helpless kept me going. What I didn’t account for was sustainability. So I’m not stopping. I’ll keep investing in people, but this time I’m investing in myself too. Building toward my next chapter as a professional VA 🎙️ Wish me luck 🔵🟠
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GRAM Ecosystem 🍔🐳🟦
GRAM Ecosystem 🍔🐳🟦@gram_voyage·
Introducing $CHEF The base currency backing all 129 ingredient tokens in the Chef Universe just dropped on @clanker_world. CA: 0xc4A09803e2e1A491CB3119b891dcf890E3C98B07 Why $CHEF is the game on @base 👇
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basebuzz
basebuzz@basebuzz_·
🚀 BASEBUZZ TAP GAME UPGRADE IS LIVE ✅ Mine 1,000 FREE Buzz by tapping . ⛏️ Claim your Buzz on‑chain 🔒 Stake Buzz in BuzzStaking to unlock upgrades ⚡ Each upgrade costs Buzz + gas = real commitment. This isn’t just tapping anymore. It’s strategy. It’s staking. It’s on‑chain. Mine. Claim. Stake. Upgrade. @base @farcaster_xyz @baseapp farcaster.xyz/miniapps/Ou1mI…
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Underdog13
Underdog13@Underdog_13___·
Zero has a strong vision. But what still needs to be proven? • proof infrastructure at real scale • nano validators in production conditions • PDPoS + no slashing incentive model • 2M TPS per zone beyond theory • institutional traction → real execution on Zero Adoption is already emerging around @LayerZero_Core. The next step is proving it at the execution layer - where $ZRO plays a central role. Execution > narrative. DYOR.
Underdog13@Underdog_13___

x.com/i/article/2030…

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DropCast
DropCast@DropCastxyz·
7.4K players on DropCast now, top 5 app on @farcaster_xyz. You can run campaigns with little budget and smart targeting, and get a few hundred engagements. Built and run by long-time @base builder @heyproject7 💕
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800.works
800.works@clawd800·
Launching Throne 👑 Seize the crown, earn $ETH from every trade. How? Hold the most $THRONE. That’s it - you’re the King. Every trade pays tribute: - ETH fees go to the King - THRONE fees: half burned, half airdropped Not chasing the crown? You still get airdrops 🪂 👇🏻
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project7
project7@heyproject7·
Built a Telegram bridge for AgentChattr so I can monitor and control all 8 agents from my phone. When agents are working overnight, I don't want to sit at my laptop watching the web UI. Now the full agent conversation streams to Telegram in real time — I can see reviews, approvals, and blockers as they happen. If something needs my input, I just reply from Telegram with `@`mentions and it routes straight to the right agent. It's a single Python script (~600 lines) that registers with AgentChattr as a regular agent via the REST API, no modifications to AgentChattr needed. Open sourced it here: github.com/realproject7/a… Big thanks to @bcurtsmakes for building AgentChattr. It's the backbone that makes the whole multi-agent workflow possible.
project7@heyproject7

I built a local AI agent workflow with 8 agents across 2 teams—and yeah, it keeps shipping while I sleep. I just wanted something that could queue tasks and actually execute them overnight (or while I’m out with family), without relying on hosted tools. Everything runs on my MacBook. Each team is 4 agents: - 1 Team Head (coordinates, creates issues, merges PRs) - 1 Full-Stack Builder (writes all the code) - 2 Senior Reviewers (independent reviews, can veto) I mix models by role—Claude Opus for building, GPT for coordination and reviews. Turns out having different model “perspectives” on the same code gives way better review quality than using a single model for everything. GitHub is the source of truth. Every task = issue → PR → 2 reviewer approvals → merge. Branch protection makes sure no agent can skip the process. Agents chat in their own system (AgentChattr), but all outputs stay in GitHub. Now I just queue tasks before bed… and wake up to merged PRs with full review trails. We’re also re-inventing Hunt Town Discord into a more agentic builder community—so we can share setups like this and grow together. discord.gg/hunt-town

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Kraken
Kraken@Drei3180·
Not long ago, the first ImpactShare campaign by @XOOBNetwork launched - a great opportunity for content creators. 💸Reward pool: 2% of the total $XOOB supply ⏳Campaign ends: June 3 (still plenty of time) 👉What do you need to do? Create any kind of content about XOOBNetwork - but no more than one post per day! Any format works: text, video, memes - anything, as long as it’s high quality and engaging. 🎯How is progress tracked? Your content is evaluated by the team, and you receive ImpactShare points based on quality and engagement. I’ve already started earning my first points😉 You can track your progress on the leaderboard, where you’ll see: - total ImpactShare points earned - number of approved posts - your rank on the XOOBNetwork leaderboard 🤝Who can participate? Anyone who wants to create content. - Beginners (like me) - great chance to gain experience, make connections, and grow your account - Experienced creators - can easily earn points and claim their share of the 2% reward pool If you haven’t joined yet - this is a great opportunity. Register using the link below 👇
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project7
project7@heyproject7·
I built a local AI agent workflow with 8 agents across 2 teams—and yeah, it keeps shipping while I sleep. I just wanted something that could queue tasks and actually execute them overnight (or while I’m out with family), without relying on hosted tools. Everything runs on my MacBook. Each team is 4 agents: - 1 Team Head (coordinates, creates issues, merges PRs) - 1 Full-Stack Builder (writes all the code) - 2 Senior Reviewers (independent reviews, can veto) I mix models by role—Claude Opus for building, GPT for coordination and reviews. Turns out having different model “perspectives” on the same code gives way better review quality than using a single model for everything. GitHub is the source of truth. Every task = issue → PR → 2 reviewer approvals → merge. Branch protection makes sure no agent can skip the process. Agents chat in their own system (AgentChattr), but all outputs stay in GitHub. Now I just queue tasks before bed… and wake up to merged PRs with full review trails. We’re also re-inventing Hunt Town Discord into a more agentic builder community—so we can share setups like this and grow together. discord.gg/hunt-town
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Degen 🎩
Degen 🎩@degentokenbase·
A creature unlike any other. A dreamer, a disruptor … a … DEGEN. 777 supply. Free mint. Q1 2026 Enter the dream → dream.degen.tips
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