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Many Base protocols - including Zora, Clanker, & Flaunch - have migrated to Uniswap v4. But few trading tools offer v4 integration. Sigma is already Base's most popular trading bot, and we've just introduced full Uniswap v4 support. What this means for Sigma users👇

12,747 views • 10 months ago •via X (Twitter)

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🎙️New pod just dropped! I sit down with my old friend Stani, Founder of Aave Labs, to deep dive into Aave v4 and its biggest updates compared to v3. This is Ep. 1 of a new series where I analyze the evolving architecture of onchain lending markets and their impact on DeFi. Just as onchain spot trading evolved from p2p models like EtherDelta to pooled AMMs like Uniswap v2—and now to modular designs like Uniswap v4, built as lower-level protocols for sophisticated actors to run custom strategies without fragmenting liquidity—lending is following a similar path. ETHLend struggled to scale its p2p fixed-rate lending approach and lacked sophisticated actors building on top of the protocol to abstract this complexity. Aave v1 introduced pooled liquidity, making it easier for retail users to borrow and lend using the same strategy dictated by the Aave DAO. Now, Aave v4 marks a new phase: a modular hub-and-spoke design for deploying bespoke credit markets. 🧩 Hubs = Capital allocators that determine rates & provide credit lines 🛠️ Spokes = isolated, configurable lending strategies that draw capital from a Hub Use cases range from RWAs to fixed-rate credit to looped LP vaults (e.g., strategies pioneered by Arrakis on Uniswap v3 + MakerDAO). Critically, Aave evolves from a vertically integrated DAO—the sole allocator of protocol capital—into a permissionless platform where institutions (e.g., BlackRock) and DAOs can co-allocate capital alongside Aave itself. This is the beginning of a modular credit layer for all of DeFi. 🎧 Listen here: 📺 Watch here: 📖 Aave v4 proposal:

Hilmar

26,014 views • 1 year ago

Cross-Chain Arbitrage on Uniswap V4 (opportunities available) I’ve always been curious: how much do prices actually differ between the “same” Uniswap V4 pool deployed across different chains? Think ETH/USDC on Ethereum vs on Unichain, Arbitrum, or Base . They’re technically the same trading pair, but entirely separate pools, with different liquidity, fees, and most importantly, prices. So I built a dashboard to monitor exactly that. It tracks real-time price discrepancies across Uniswap V4 pools on different chains. Why? I was curious. I wanted to understand: - How often do mispricings occur? - How big are those discrepancies? - And perhaps most interestingly: how fast are they arbitraged away? On the same chain, arbitrage between DEXs is well understood. You can create a smart contract that executes a set of swaps atomically, either guaranteeing profit or reverting the transaction. But cross-chain? That’s a different game. Different chains mean different execution environments, timing issues, and gas fee structures. It’s harder to coordinate. It’s also harder to monitor... What I’ve Built (So Far) Right now, the dashboard monitors the ETH/USDC pair across four chains in real time. You can visually see: - Realtime ETH price on each chain - Size of most recent trades indicated by bubble size - Max current spread available. I've only finished it this afternoon and after watching it for 15min I've already spotted a few times where the mispricings are significant. I want to add more pairs and chains soon (super easy actually) - just don't want to clutter it before getting more feedback and ideas. I’ve dropped the link to the dashboard in the comments below. If you’ve built a cross-chain arb bot, I’d love to hear how you approached it!

jonjon

45,127 views • 1 year ago

FLOKI TRADING BOT IS OFFICIALLY LIVE ON MAINNET Floki Trading Bot is the fastest and simplest way to buy and manage tokens — directly from Telegram — without having to deal with slow DEXs, transaction confirmation delays, or slippage issues that cost you money. Often, this could mean the difference between making a mere 1x or a 5x on the same trade. Floki Trading Bot is initially launching on the mainnet of the three biggest EVM chains: BNB, Ethereum, and Base. It is also set to go live on Solana and other top EVM chains soon! You can learn more here: Some of the key features of the Floki Trading Bot include: - Super fast token purchases in a few taps/seconds on any device directly from your Telegram app. - Easily buy hyped tokens/launches in a few taps without having to deal with slow DEXs. You can also buy tokens very quickly based on important news/announcements i.e. faster than it takes the average person to load their browser. This gives you a MASSIVE edge over others! - Multi-chain support i.e. you can seamlessly buy tokens across several different blockchains all in one place. - Multi-language support allows you to use the bot in your native language: supported languages include English, simplified Chinese, traditional Chinese, Turkish, Russian, Indonesian, Spanish, Dutch & more to come. - Seamless integration with top DEX aggregators to ensure you get the best token prices. - Create up to 5 wallets. You can create/export/delete up to 5 wallets. - Easily withdraw funds from the bot to third party wallets e.g. CEXs or on-chain wallets. - Referral program allows you to refer your friends and earn a commission whenever they trade through Floki Trading Bot. - And a lot more... In addition to the above key features, you can also unlock EXCLUSIVE rewards by using Floki Trading Bot. This includes airdrops, trading incentives, and special deals exclusive to Floki Trading Bot users. This is due to Floki's strong dominance in the industry which allows us to bring strong value to users of the Floki Trading Bot. For example, the most anticipated memecoin launch of this year, Simon's Cat, allocated 1.5% of their token supply EXCLUSIVELY to Floki Trading Bot users who trade their token with Floki Trading Bot. No other bot in the industry got this opportunity, and it's just one of the many exclusive rewards we have planned for users of the Floki Trading Bot! You can learn more about Floki Trading Bot here:

FLOKI

281,441 views • 1 year ago

We are super pleased to share the 11th PA to the #Flux ecosystem, Coinbase 🛡️ Base! Flux-Base will provide some fantastic opportunities for us in a new and developing ecosystem. Here are a few items that will deliver interchain operability: Bridging Flux blockchain to Base Build, Coinbase's Layer 2 solution, offers several massive advantages: 1. **Access to a Wider User Base**: Coinbase has a large and established user base. By bridging with Base, flux can tap into this user pool, gaining exposure and adoption among a broader audience. 2. **EVM Compatibility**: Base is EVM-compatible, meaning it can seamlessly interact with #Ethereum-based assets and services. This compatibility allows for easier integration and interoperability between Flux and the Base ecosystem. 3. Innovation and Dapp Development: Base's environment is conducive to decentralized application (dApp) development. Bridging with Base opens up opportunities for developers on your blockchain to innovate and create dApps that can leverage Flux and Base's capabilities. 4. Seamless Integration with Coinbase Products: Base offers seamless integration with various Coinbase products. This integration can provide Flux access to advanced tools and services offered by Coinbase, enhancing the overall functionality and user experience. 5. Increased Liquidity: Bridging with Base can increase the liquidity of Flux, providing additional avenues for trading and exchange within the Coinbase ecosystem. So, bridging with Base can provide scalability, cost-efficiency, security, and access to a broader ecosystem that will be highly beneficial for the growth and adoption of Flux. Learn more here: #web3 #blockchain #CloudComputing #DePIN

Flux I Decentralized Cloud

148,498 views • 2 years ago

I have been testing DeepSeek-V4-Pro with the Pi coding agent. I am mindblown by how well it works out of the box. A few notes: I spent a few hours building an LLM wiki with an agent powered entirely by DeepSeek-V4-Pro on Fireworks AI inference. This is the first time I feel like there is an open-weight model that can reason at the level of Claude and Codex. And it does this in a cost-effective way with support for 1M context length. To be clear, I am using DeepSeek-V4-Pro inside of Pi without any special configuration. It works out of the box. It's exciting that there is a model that can just be plugged into a basic harness like Pi, and it just works. I've never seen that before. Most models require lots of configuration and setup. DeepSeek's DeepSeek-V4-Pro is clearly good at agentic coding (probably the best from the open-weight models), but the model is also great on knowledge-intensive tasks where reasoning matters. The agent pulled agentic engineering best practices from different company docs (Anthropic, OpenAI, Google, Stripe, Meta, Modal, DeepSeek, Mistral, Cohere), searched and digested Reddit and HN threads, summarized arxiv papers, and surfaced trending GitHub repos. Then it distilled everything into actionable tips across categories. I love the Wiki it built. The quality is really good. Here is a snapshot of what the wiki looks like: DeepSeek-V4-Pro handled the task without breaking stride. Multi-step research queries, code generation for scaffolding, context-heavy reasoning across disparate sources. For coding specifically, this is the first open-weight model that genuinely feels like a Codex or Claude Code experience. It compares in capability and actual multi-turn agentic work. What made the loop feel so responsive was Fireworks' inference speed (the fastest in the market) and the fact that they actually validate models at the systems level before shipping. No corrupted reasoning traces. Just fast, reliable iteration. The hybrid CSA and HCA attention design cuts KV cache to just 10% and inference FLOPs by nearly 4x at 1M-token context. This is what makes the agent loop actually fast and cheap enough to run in practice. For devs who've been watching open-weight models close the gap but haven't found one that actually delivers in practice, this is the closest I've seen. Try it here:

elvis

59,426 views • 2 months ago