Jordi Corbilla 🇬🇧 (@thunderjordi) header
Jordi Corbilla 🇬🇧 (@thunderjordi) profile picture

Practical engineering notes on systems, automation, data workflows, and applied AI.

Joined May 2009
Photo by Jordi Corbilla 🇬🇧 (@thunderjordi)Photo by Jordi Corbilla 🇬🇧 (@thunderjordi)Photo by Jordi Corbilla 🇬🇧 (@thunderjordi)Photo by Jordi Corbilla 🇬🇧 (@thunderjordi)Photo by Jordi Corbilla 🇬🇧 (@thunderjordi)Photo by Jordi Corbilla 🇬🇧 (@thunderjordi)Photo by Jordi Corbilla 🇬🇧 (@thunderjordi)
Jordi Corbilla 🇬🇧 retweeted
Microsoft open-sourced an app that watches you do a task once, then teaches your AI agent to do it for you. 4.3K stars. How Skill Recorder works: 1. Hit record (⌘⇧R or Ctrl+Shift+R) and do the task normally. You can talk through it out loud too. 2. It captures your clicks, app switches, pages and terminal commands, all on your machine. 3. GitHub Copilot turns the recording into a list of steps you can edit. 4. One click turns it into a skill your agent can run, or an automation on a schedule. It generalizes from one example, so recording yourself submitting one form teaches the agent to submit all of them. Runs on Mac, Windows 11 and Ubuntu. Needs a GitHub account with Copilot access. Install: copy the one-line command from the latest release: github.com/microsoft/skill-r…
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Jordi Corbilla 🇬🇧 retweeted
THIS IS A PURE F*CKING GOLDMINE Anthropic just shared one of the best tricks for Claude Code. The idea: let Opus 5.5 write the plan as a script, and let a swarm of cheaper agents run it. Bigger jobs and way fewer Opus tokens, because the script holds the plan and Opus only sees the final answer. You just launch Claude Code like this: claude --model opus --effort ultracode here's the split: → Opus 5.5 turns your task into a workflow script → Haiku 5.5 agents scan every file in parallel → Sonnet 5.5 agents patch each fix in its own isolated copy → you stop burning Opus context on a hundred agent transcripts Opus only shows up at three moments: → writing the workflow before the run → reading the verified results after it → signing off before anything ships Opus plans. Haiku scans. Sonnet ships. JEV engineering is the same move one layer down: the forks that need no thinker (which file, which tool, retry or stop) go to JEV in milliseconds, and the big models only see the ones that split the full tree > Opus 5.5 runs the main session and writes the workflow > scan audits every file on Haiku 5.5 > edit patches each fix on Sonnet 5.5 in an isolated copy > verify cross-checks every finding on Sonnet 5.5 > Opus 5.5 reads only what survived verification > JEV routes micro-forks in milliseconds paste the tree and this prompt into Claude Code ↓ "Rebuild my Claude Code setup around this tree: 1. Run the main session on Opus 5.5 with ultracode on. 2. In every workflow, run read-only scan stages on Haiku 5.5 and edit stages on Sonnet 5.5, each edit in its own isolated copy. 3. Add one rule to CLAUDE.md: every finding gets an independent verifier agent before it's reported, and fix loops stop after two rounds with no progress. 4. Route fast deterministic forks (tool choice, path selection, retries) through JEV. Show me every change as a diff first. No edits until I say go." ↳ code.claude.com/docs/en/work… save it, it'll come in handy.
REA is the most dangerous repo on GitHub right now It gives your AI agent a real disassembler, so it reads any app down to the binary Every claim comes back with the address behind it I broke down the exact 8 steps:
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Jordi Corbilla 🇬🇧 retweeted
Hollywood tem tudo para temer Inteligência artificial
🤖 Made with AI
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Jordi Corbilla 🇬🇧 retweeted
⚡️BREAKING: The Emir of Qatar is asking his Citizens to prepare for the Worst-Case Scenarios in the Middle East "We must be prepared for the worst possibilities and everyone must bear their responsibilities, institutions and citizens"
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Jordi Corbilla 🇬🇧 retweeted
this is the most DANGEROUS thing on internet today REA (Reverse Engineer Anything) - this open source MCP lets your AI agent reverse engineer any app, down to the binary i just reverse engineered TradingView, now I am using their premium indicators for FREE - software is cooked here is how to use REA to reverse engineer TradingView: 1. point REA at TradingView's publicly observable interface and map every major feature, from chart interactions to indicators and alerts 2. trace how the interface behaves when you switch symbols, change timeframes, add indicators, draw levels, and update chart layouts 3. use REA's available MCP tools to investigate authorized software artifacts and trace application logic wherever the evidence allows 4. feed those findings into Claude Opus 5.5, which turns the investigation into a technical specification and an implementation plan 5. rebuild the charting engine independently, with real market data, candlesticks, technical indicators, drawing tools, and persistent watchlists 6. add a quant backtesting engine that tests strategies against historical data, accounting for fees, slippage, and drawdowns 7. test every component, compare its behavior against documented and observable results, fix failures, and keep iterating until the system actually works 8. you end up building your own TradingView-style quant research terminal from scratch, with source code you control and features you can keep extending i won’t say anything more:
Jev is the FASTEST AI model ever built for trading It makes calibrated buy/sell decisions in under 100 ms That is one real decision on every single block, 24/7 In this article I've shown EXACTLY how to build HFT trading system with Jev (from scratch)
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Jordi Corbilla 🇬🇧 retweeted
#1 on github trending now...I do what I can😂
Reverse Engineer Anything 😂 Software is done.
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Jordi Corbilla 🇬🇧 retweeted
Fanqie Jiang Ye came up with an ingenious way to make money from her streams: charging people to spin her around It reportedly costs around $7.50 per spin I dread to think what the next thing to top this will be Writer: Ian
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Jordi Corbilla 🇬🇧 retweeted
Introducing Microsoft-Decision-1, our new model for fast decision-making. It delivers top performance on structured decision tasks, outperforming both LLMs and other decision models in latency and quality. We’re already testing it across Microsoft for everything from incident response and quality control to scientific discovery.
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Jordi Corbilla 🇬🇧 retweeted
Searching 32 million records in 30 milliseconds is insane! @typesense is a crazy fast open-source search engine that supports natural language search. It's like Elasticsearch but much easier to set up, configure, and use. You can do all of this with Typesense: • Fuzzy search: Typo-tolerant fuzzy search-as-you-type • Vector search: Return records that are similar to each other • Semantic search: Return records that are conceptually related • Geo Search: Return records that are close to a given lat/long Typesense's Natural Language Search is pretty cool: It turns a sentence into a structured search query, then runs it against your data. For example: The query "Find me a BMW under $40,000, newer than 2020, with the lowest mileage first" will produce the following: • Filters: brand = BMW, price < 40000, year > 2020 • Sort: mileage ascending Typesense will then apply these filters and sorting rules to the data. They offer client libraries in Python, JavaScript, PHP, Ruby, Go, Rust, Swift, .NET, Java, and pretty much any other programming language. You can start searching in less than a minute: 1. Run their Docker image (or one of their native binaries) 2. Import your data 3. Start searching! GitHub repository: fandf.co/4ytzmmE I recorded a quick video to show you a demo. I promise you haven't seen anything this fast! Thanks to the team for partnering with me on this post.
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Jordi Corbilla 🇬🇧 retweeted
Introducing Microsoft-Decision-1 Our model for fast decision-making that delivers top performance in latency and quality on structured decision tasks to outperform both LLMs and other decision models. commandline.microsoft.com/mi…
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Jordi Corbilla 🇬🇧 retweeted
Little bugger 😂😂😂
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Following cells through time: our Biohub journey into the bronze range thundaxsoftware.blogspot.com…
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Jordi Corbilla 🇬🇧 retweeted
So the fastest JSON serializer for .NET now is developed by CAPCOM as part of their game engine that powers Resident Evil / Biohazard I did not see that coming lol github.com/CAPCOM-TD-OSS/RED…
シリアライザーの話を聞きにきました
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Jordi Corbilla 🇬🇧 retweeted
WSL containers is now generally available. Build, run and deploy Linux containers directly on Windows with the WSL containers CLI and API 🧵
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Jordi Corbilla 🇬🇧 retweeted
A fine-tuned model can outperform frontier models and be cheaper and faster to run. Literally, every company I've met wants this. I want you to see these results from fine-tuning Qwen3 4B on AWS. It smokes both the out-of-the-box model and Claude Sonnet 4.6.
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Jordi Corbilla 🇬🇧 retweeted
Ollama now supports Jev-like decision models all locally. Use decision models like Nimble for tasks like ticket triaging, model routing, and content moderation. ollama pull nimble Here’s Nimble playing Ollama racer through the new local /v1/systemone API by making decisions in real-time. 🏎️
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Jordi Corbilla 🇬🇧 retweeted
🚨 بروفيسور في جامعة ستانفورد يحول 100 ألف دولار إلى 7 ملايين دولار بدون ما يتوقع سعر سهم واحد فقط! 🤯📉📈 ​الرجل أخذ سهمين عاديين جداً في السوق، وكل اللي كان يعمله هو إعادة توازن يومية... والنتيجة كانت ثروة طائلة وبدون أي تكهنات أو تحليل فني معقد! 💰⚡️ السر ليس في "التنبؤ بالمستقبل"، بل في اتباع استراتيجية رياضية صارمة تستغل تقلبات السوق لصالحك! ​💡 هذه الدقائق المعدودة قد تغير نظرتك للاستثمار والتداول للأبد. ​شاهد الفيديو، احفظ التغريدة عندك الآن، وشاركها مع كل مهتم بالتداول والأعمال! 🔖🧵👇
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Jordi Corbilla 🇬🇧 retweeted
First person to get 3 cones wins. A very tired mother invented this game! 😂😂
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Jordi Corbilla 🇬🇧 retweeted
we're hiring senior engineers (preferably math PhDs) with 10+ years of experience with Opus 5.5
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