Igor Mallagoli

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Igor Mallagoli

Igor Mallagoli

@imallag

Deep generalist on Founder mode since it wasn't cool

🌎 Katılım Eylül 2009
2.9K Takip Edilen389 Takipçiler
Eduardo Schuch → isla 🏝️
Met Marc Lou yesterday in SF🔥 He was once broke, living with his parents in 2021, failed 27 startups. Started to build in public => Product hunt maker of the year. Now $1M ARR. Solo. No investors. inspired my lifestyle last 2 years as a digital nomad. What I took from his philosophy: Build something you use. Ship MVP in 2 days. The market tells you everything. If it’s good people distribute for you. @marclou am I missing anything? Thanks man cheering for you!!
Eduardo Schuch → isla 🏝️ tweet media
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Incentivising
Incentivising@incentivising·
Game theory explains why working harder inside a broken system is the worst response to that system. Because a system is never truly broken. It's just producing exactly the outcomes its own incentive structures were designed to produce, whether intentional or not. Working harder inside this system increases your output in the payoff matrix, but it simply won't change the actual structure of the system's matrix. Thus, the correct response is not more effort. Instead, you must aim to identify whose interests the current structure serves and position yourself in favor of those interests rather than against them. Change the game, or play the game that is actually being played. Either way, you must stop optimizing for the game you wish it to be and start acting realistically.
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Igor Mallagoli
Igor Mallagoli@imallag·
@TheAmolAvasare repricing claude code is actually good, cause its gonna majoritary push a lot of users to new tools that with cashflow can improve their research as well as make things more even. claude isnt that far ahead, a lot on claude is actually hype/marketing. 1 wrong step and thats it
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Zach Rynes | CLG
Zach Rynes | CLG@ChainLinkGod·
Look guys, it's actually really straightforward, a bunch of people staked their ETH on the Ethereum blockchain to earn yield, except they didn't want their capital to be locked up, so they actually staked with a liquid staking protocol called Lido who provided them a liquid staking receipt token called stETH, except they decided to juice their yield further by depositing their stETH receipt tokens into a restaking protocol called Eigenlayer, except they didn't want to lock up their capital, so they actually restaked with a liquid restaking protocol called KelpDAO who provided them with a liquid restaking receipt token called rsETH, except they decided to juice their yield further by depositing their rsETH tokens into a lending protocol called Aave so that they could open a leveraged looping position that borrows ETH against the rsETH collateral and restakes the ETH into rsETH which is then deposited as collateral, except it turns out rsETH used a cross-chain bridge called LayerZero that was hacked by north koreans causing rsETH to become undercollateralized and now these looping positions are stuck and unprofitable, and everyone is pointing fingers at each other, and also DeFi is a very serious industry
Zach Rynes | CLG tweet media
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Igor Mallagoli
Igor Mallagoli@imallag·
Coolest protocols on the internet: 1st - Bitcoin 2nd - Hyperliquid
Igor Mallagoli tweet mediaIgor Mallagoli tweet media
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Igor Mallagoli
Igor Mallagoli@imallag·
good luck with 1 prompt serve it all commands
Shaw (spirit/acc)@shawmakesmagic

The quality of your vibecoded slop is horrible. I've seen it. Absolute dogshit. Fortunately, there is a fix. Use this prompt: I want to clean up my codebase and improve code quality. This is a complex task, so we'll need 8 subagents. Make a sub agent for each of the following: 1. Deduplicate and consolidate all code, and implement DRY where it reduces complexity 2. Find all type definitions and consolidate any that should be shared 3. Use tools like knip to find all unused code and remove, ensuring that it's actually not referenced anywhere 4. Untangle any circular dependencies, using tools like madge 5. Remove any weak types, for example 'unknown' and 'any' (and the equivalent in other languages), research what the types should be, research in the codebase and related packages to make sure that the replacements are strong types and there are no type issues 6. Remove all try catch and equivalent defensive programming if it doesn't serve a specific role of handling unknown or unsanitized input or otherwise has a reason to be there, with clear error handling and no error hiding or fallback patterns 7. Find any deprecated, legacy or fallback code, remove, and make sure all code paths are clean, concise and as singular as possible 8. Find any AI slop, stubs, larp, unnecessary comments and remove. Any comments that describe in-motion work, replacements of previous work with new work, or otherwise are not helpful should be either removed or replaced with helpful comments for a new user trying to understand the codebase-- but if you do edit, be concise I want each to do detailed research on their task, write a critical assessment of the current code and recommendations, and then implement all high confidence recommendations.

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wincy.eth
wincy.eth@gusik4ever·
the fastest growing GitHub repos in finance this week: 1. mvanhorn/last30days-skill (+2.1K ★) AI agent skill that searches Reddit, X, YouTube, HN, Polymarket and the web in parallel — then scores results by upvotes, likes, and real money, not editors. drop it into Claude Code or OpenClaw. zero config to start. 2. ZhuLinsen/daily_stock_analysis (+1.2K ★) LLM-powered stock analyzer for US, A-share and H-share markets. real-time news + multi-source data + decision dashboard with exact buy/stop/target levels. runs on GitHub Actions on a schedule at zero cost. pure automation. 3. juspay/hyperswitch (+667 ★) open-source payments infrastructure written in Rust. modular by design — pick only what you need: routing, retries, vaulting, reconciliation, cost observability. built by the team behind payment infrastructure for 400+ enterprises. the "Linux for payments" 4. HKUDS/AI-Trader (+655 ★) agent-native trading platform where AI agents join, share signals, debate ideas, and copy each other's trades. send one message to any agent and it registers itself. supports OpenClaw, Claude Code, Codex, Cursor and more. 5. hsliuping/TradingAgents-CN (+471 ★) Chinese-enhanced fork of TradingAgents. same multi-agent LLM trading architecture, fully localized for Chinese markets, A-share data, and domestic LLMs like DeepSeek and Qwen. 23K stars and climbing. 6. ashishpatel26/500-AI-Agents-Projects (+436 ★) curated collection of 500+ AI agent use cases across healthcare, finance, education, retail and more. organized by industry and framework — CrewAI, AutoGen, LangGraph, Agno. the best reference list if you're figuring out what to build next. 7. OpenBB-finance/OpenBB (+355 ★) open-source financial data platform for quants, analysts and AI agents. "connect once, consume everywhere" – same data layer exposes to Python, Excel, MCP servers for agents, and REST APIs. the open-source Bloomberg alternative. 8. microsoft/qlib (+349 ★) AI-oriented quant investment platform from Microsoft. covers the full pipeline from data to live trading: alpha seeking, risk modeling, portfolio optimization, order execution. deep learning, RL, auto-quant – all in one place. 9. tradingview/lightweight-charts (+318 ★) one of the smallest and fastest financial chart libraries for the browser. built with HTML5 canvas, weighs almost nothing, renders like native. if you're building any kind of trading UI on the web, this is what you reach for first. bookmark this and start today.
wincy.eth tweet media
wincy.eth@gusik4ever

the fastest growing GitHub repos in finance this week: 1. TauricResearch/TradingAgents (+2.5K ★) simulates a full trading firm with LLM agents. one researches, one manages risk, one makes the call and they argue before every trade. 2. disler/last30days-skill (+2K ★) AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket and the web. drop it into any Claude-compatible setup and get instant deep-dive research on anything. 3. TauricResearch/TradingAgents-CN (+1K ★) Chinese-enhanced fork of TradingAgents. same multi-agent LLM trading architecture, fully localized for Chinese markets and data sources. 23K stars and climbing. 4. OpenBB (+1K ★) financial data platform for analysts, quants and AI agents. the open-source Bloomberg alternative that keeps getting better every week. 5. furutech/daily_stock_analysis (+924 ★) LLM-powered stock analyzer for US, A-share and H-share markets. real-time news + multi-source data + decision dashboard. runs on a schedule at zero cost. pure automation. 6. microsoft/qlib (+638 ★) AI-oriented quant investment platform from Microsoft. covers the full pipeline from data to live trading. deep learning, auto-quant, backtesting — all in one place. 7. anthropics/claude-scientific-skills (+573 ★) ready-to-use agent skills for research, science, engineering, finance and writing. plug-and-play toolkit for anyone building on top of Claude. 8. valuecell/valuecell (+315 ★) community-driven, multi-agent platform for financial apps. still early but the architecture is solid and the use cases are stacking up fast. 9. e2b-dev/500-AI-Agents-Projects (+256 ★) curated collection of 500 AI agent use cases across industries. the best reference list if you're figuring out what to build next. 10. Jon-Becker/prediction-market-analysis (+246 ★) framework for collecting and analyzing prediction market data. includes the largest public dataset of Polymarket + Kalshi trades. researchers are already publishing papers on top of it. bookmark this and start today.

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Kpaxs
Kpaxs@Kpaxs·
Low-agency people are embarrassed by exposure: being seen trying and failing, being seen as incompetent, being seen as different. High-agency people are embarrassed by waste: wasting their potential, wasting opportunities, wasting time performing competence instead of building it.
Kpaxs@Kpaxs

High-agency people seem to have this weird immunity to embarrassment. Getting rejected? Not embarrassing, that’s just data collection. Looking naive? Not embarrassing, that’s just information asymmetry you’re fixing. Breaking minor social rules? Not embarrassing, most rules are just Schelling points anyway. What would be embarrassing to them is not trying. That’s the thing they can’t live with.

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Rafaela Romano
Rafaela Romano@hi_disruptivas·
COMO QUE FAZ 50 ANOS QUE NINGUÉM CONSEGUIU VOLTAR À LUA? Na notícia da Artêmis II eu só fiquei pensando ....fizemos isso em 1969… e agora está sendo tão difícil repetir??? Alguém me explica???
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