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2/ Check out how Gemini 3.5 Flash instantly digests dense academic papers and autonomously codes a fully interactive, visual website explaining the intricacies of the research. It's an incredible stress test that seamlessly merges massive long context, deep reasoning, complex coding, and ultra-low latency. It really helps you distill...

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Jeff Dean

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88,949 просмотров • 3 месяцев назад •via X (Twitter)

ОбразованиеНаука и технологии
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Following our Gemini 3.5 Flash launch at #GoogleIO, check out this demo of what it can do right inside the Gemini app. Watch 3.5 Flash build an interactive circuit helper, outputting a step-by-step physical build guide alongside a working simulation. It’s a great example of how the Gemini app can help students learn visually, breaking down new or complex concepts and guiding them through hands-on subjects interactively. 3.5 Flash is available globally today!
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Following our Gemini 3.5 Flash launch at #GoogleIO, check out this demo of what it can do right inside the Gemini app. Watch 3.5 Flash build an interactive circuit helper, outputting a step-by-step physical build guide alongside a working simulation. It’s a great example of how the Gemini app can help students learn visually, breaking down new or complex concepts and guiding them through hands-on subjects interactively. 3.5 Flash is available globally today!

koray kavukcuoglu

59,439 просмотров • 3 месяцев назад

ROUTE LLM - SUPER INTELLIGENCE IN THE MAKING RouteLLM combines the superpowers of o1-preview, Sonnet 3.5, Gemini Flash, o1-mini, 4o, and 4o-mini! Based on your query, it optimizes for latency, cost, and quality of the response. The cool thing about RouteLLM is it combines. - coding ability of Sonnet 3.5 - reasoning ability of o1-preview - hard-coding ability of o1-mini - 1M context of Gemini - speed of 4o-mini Similar to ensemble models that are very effective in classical ML, RouteLLM and similar models will be the most effective in the LLM ecosystem
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ROUTE LLM - SUPER INTELLIGENCE IN THE MAKING RouteLLM combines the superpowers of o1-preview, Sonnet 3.5, Gemini Flash, o1-mini, 4o, and 4o-mini! Based on your query, it optimizes for latency, cost, and quality of the response. The cool thing about RouteLLM is it combines. - coding ability of Sonnet 3.5 - reasoning ability of o1-preview - hard-coding ability of o1-mini - 1M context of Gemini - speed of 4o-mini Similar to ensemble models that are very effective in classical ML, RouteLLM and similar models will be the most effective in the LLM ecosystem

Bindu Reddy

66,374 просмотров • 1 год назад

Sci-Hub is an evil website that pirated 85M+ research papers and made them freely available And now they've added AI to their database to make Sci-Bot. It answers your questions using latest, full-text articles. But DO NOT use it. We should all try to make billion-dollar academic publishers richer. I'm putting the link below so you know how to avoid it.
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Sci-Hub is an evil website that pirated 85M+ research papers and made them freely available And now they've added AI to their database to make Sci-Bot. It answers your questions using latest, full-text articles. But DO NOT use it. We should all try to make billion-dollar academic publishers richer. I'm putting the link below so you know how to avoid it.

Mushtaq Bilal, PhD

5,036,960 просмотров • 4 месяцев назад

PhD Students – How to easily understand a complex research topic? Meet Ponder – a tool for understanding complex research. 𝐇𝐨𝐰 𝐏𝐨𝐧𝐝𝐞𝐫 𝐰𝐨𝐫𝐤𝐬? 1. Go to and log in 2. Enter your research topic or research question 3. Ponder will start building a knowledge map 4. This knowledge map breaks down complex ideas into structured cards 𝐖𝐡𝐚𝐭 𝐜𝐚𝐧 𝐲𝐨𝐮 𝐝𝐨 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞𝐬𝐞 𝐜𝐚𝐫𝐝𝐬? → You can add your own thoughts, questions, and insights. → Ask follow-up questions and deepen your exploration. → You can color the cards for better understanding → You can drag & organize them freely across the infinite canvas. 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐝𝐝 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐩𝐚𝐩𝐞𝐫𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐜𝐚𝐫𝐝𝐬? — You can search for relevant papers with built-in discovery. — Ponder will identify all relevant papers — You can then add or upload research papers — You can also attach papers to specific cards. 𝐀𝐟𝐭𝐞𝐫 𝐲𝐨𝐮𝐫 𝐩𝐨𝐧𝐝𝐞𝐫𝐢𝐧𝐠 𝐢𝐬 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐝: ➟ You can change the view to document, browser, or full screen. ➟ You can also download your knowledge map as a PDF ➟ You can ask further questions and refine with Ponder’s Agent. 𝐖𝐡𝐚𝐭 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐭𝐡𝐢𝐬 𝐰𝐚𝐲 𝐢𝐬 𝟏𝟎𝐱 𝐛𝐞𝐭𝐭𝐞𝐫? ↳ It brings discovery and analysis of research into one workspace ↳ It makes ideas branch and evolve naturally, just like your brain ↳ It helps you to easily identify research gaps ↳ It connects knowledge from all sources such as papers and web ↳ It enables you to export knowledge as maps, reports, or data. ↳ Designed for PhD students & researchers, who think deeply. Try Ponder here: Anything you'd like to add?
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PhD Students – How to easily understand a complex research topic? Meet Ponder – a tool for understanding complex research. 𝐇𝐨𝐰 𝐏𝐨𝐧𝐝𝐞𝐫 𝐰𝐨𝐫𝐤𝐬? 1. Go to and log in 2. Enter your research topic or research question 3. Ponder will start building a knowledge map 4. This knowledge map breaks down complex ideas into structured cards 𝐖𝐡𝐚𝐭 𝐜𝐚𝐧 𝐲𝐨𝐮 𝐝𝐨 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞𝐬𝐞 𝐜𝐚𝐫𝐝𝐬? → You can add your own thoughts, questions, and insights. → Ask follow-up questions and deepen your exploration. → You can color the cards for better understanding → You can drag & organize them freely across the infinite canvas. 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐝𝐝 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐩𝐚𝐩𝐞𝐫𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐜𝐚𝐫𝐝𝐬? — You can search for relevant papers with built-in discovery. — Ponder will identify all relevant papers — You can then add or upload research papers — You can also attach papers to specific cards. 𝐀𝐟𝐭𝐞𝐫 𝐲𝐨𝐮𝐫 𝐩𝐨𝐧𝐝𝐞𝐫𝐢𝐧𝐠 𝐢𝐬 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐝: ➟ You can change the view to document, browser, or full screen. ➟ You can also download your knowledge map as a PDF ➟ You can ask further questions and refine with Ponder’s Agent. 𝐖𝐡𝐚𝐭 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐭𝐡𝐢𝐬 𝐰𝐚𝐲 𝐢𝐬 𝟏𝟎𝐱 𝐛𝐞𝐭𝐭𝐞𝐫? ↳ It brings discovery and analysis of research into one workspace ↳ It makes ideas branch and evolve naturally, just like your brain ↳ It helps you to easily identify research gaps ↳ It connects knowledge from all sources such as papers and web ↳ It enables you to export knowledge as maps, reports, or data. ↳ Designed for PhD students & researchers, who think deeply. Try Ponder here: Anything you'd like to add?

Faheem Ullah

12,175 просмотров • 1 год назад

Systematic literature reviews take 12-18 months to complete. Looks like AI is going to fully automate systematic reviews sooner than later. SciSpace ( SciSpace) just launched an autonomous AI agent that conducts a systematic literature review with a single prompt. Go to scispace[.]com and run the following prompt: "Conduct a systematic literature review on [your topic]" SciSpace agent will generate research questions based on the PICO framework. You can review these questions and edit them according to your specific requirements. The agent will also draft screening criteria that you can edit according to your needs. Then the agent asks you to select the databases you want to use and the date range for paper. After this step, everything is fully automated. The agent will search for papers in the relevant databases, it will combine and rerank the papers. Then it will start the title and abstract screening and include the papers that meet the include criteria. In the next step, it will download the full text of included papers and screen them followed by data extraction. Based on the extracted data, it generates a complete systematic literature review and also a PRISMA diagram. It will also give you a table of papers included along with the rational for including them. The only thing that is keeping AI agents to fully automate systematic literature reviews fields is the papers behind paywalls. Check out the agent at scispace[.]com and see if you find its review useful.
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Systematic literature reviews take 12-18 months to complete. Looks like AI is going to fully automate systematic reviews sooner than later. SciSpace ( SciSpace) just launched an autonomous AI agent that conducts a systematic literature review with a single prompt. Go to scispace[.]com and run the following prompt: "Conduct a systematic literature review on [your topic]" SciSpace agent will generate research questions based on the PICO framework. You can review these questions and edit them according to your specific requirements. The agent will also draft screening criteria that you can edit according to your needs. Then the agent asks you to select the databases you want to use and the date range for paper. After this step, everything is fully automated. The agent will search for papers in the relevant databases, it will combine and rerank the papers. Then it will start the title and abstract screening and include the papers that meet the include criteria. In the next step, it will download the full text of included papers and screen them followed by data extraction. Based on the extracted data, it generates a complete systematic literature review and also a PRISMA diagram. It will also give you a table of papers included along with the rational for including them. The only thing that is keeping AI agents to fully automate systematic literature reviews fields is the papers behind paywalls. Check out the agent at scispace[.]com and see if you find its review useful.

Mushtaq Bilal, PhD

42,003 просмотров • 4 месяцев назад

I tried a new AI IDE. After seeing Qoder pop up in my feed, I got fomo and decided to try it. It's more than a coding assistant: a tool with a deep understanding of your codebase. I created six prompts to test it, and I was REALLY impressed Check the 6 Videos below.
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I tried a new AI IDE. After seeing Qoder pop up in my feed, I got fomo and decided to try it. It's more than a coding assistant: a tool with a deep understanding of your codebase. I created six prompts to test it, and I was REALLY impressed Check the 6 Videos below.

Francesco Ciulla

161,994 просмотров • 11 месяцев назад

✨ What makes Gemini 2.5 Pro stand out? In this Release Notes episode, Sr. Product Manager Logan Kilpatrick and Gemini Product Lead Tulsee Doshi break down its reasoning, coding, and multimodal strengths, plus a 1M token long context. ↓ Timecodes: 1:05 Gemini 2.5 launch overview 3:19 Academic evals vs. vibe checks 6:19 The jump to 2.5 7:51 Coordinating cross-stack improvements 11:48 Role of pre/post-training vs. test-time compute 13:21 Shipping Gemini 2.5 15:29 Embedded safety process 17:28 Multimodal reasoning with Gemini 2.5 18:55 Benchmark deep dive 22:07 What’s next for Gemini 24:49 Dynamic thinking in Gemini 2.5 25:37 The team effort behind the launch
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✨ What makes Gemini 2.5 Pro stand out? In this Release Notes episode, Sr. Product Manager Logan Kilpatrick and Gemini Product Lead Tulsee Doshi break down its reasoning, coding, and multimodal strengths, plus a 1M token long context. ↓ Timecodes: 1:05 Gemini 2.5 launch overview 3:19 Academic evals vs. vibe checks 6:19 The jump to 2.5 7:51 Coordinating cross-stack improvements 11:48 Role of pre/post-training vs. test-time compute 13:21 Shipping Gemini 2.5 15:29 Embedded safety process 17:28 Multimodal reasoning with Gemini 2.5 18:55 Benchmark deep dive 22:07 What’s next for Gemini 24:49 Dynamic thinking in Gemini 2.5 25:37 The team effort behind the launch

Google AI Developers

228,165 просмотров • 1 год назад

CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇
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CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇

Md Riyazuddin

31,199 просмотров • 2 месяцев назад

Two big steps towards our vision for @NotebookLM as the ultimate research platform: • Integrating Deep Research, with a set of only-at-Notebook features that let you explore the retrieved sources • Launching a series of Featured Notebooks curated by Google Research These developments are designed to enhance the full life cycle of research and scholarship: using the power of AI to assemble the knowledge base you need to advance your understanding, and then making your work accessible and intelligible to a wider audience using all the explanatory tools that Notebook offers. If you've used DeepResearch in the Gemini app, you already know that it's a pioneering advance in assembling complex, grounded information on any topic imaginable—collecting an entire trove of material for you and writing a nuanced research report that summarizes the findings. But because NotebookLM is designed to manage and explore potentially hundreds of sources, the Deep Research report is only the beginning of your journey. In our integration, Deep Research gives you an overview all of the sources it found during its research phase, with annotated commentary explaining how each source related to your original query. You can then choose to import some or all of the sources to the notebook, along with the report itself, which you can then explore or transform using the full suite of tools that Notebook offers: grounded chat with citations, Mind Maps, Audio/Video overviews, and much more. And it's that suite of tools that make the Google Research Featured Notebooks so compelling as well. Each notebook contains a curated collection of articles on a specific topic, published by the Google Research team. Think of them as a kind of knowledge base of Google's best thinking on a series of compelling research questions: How do scientists link genetics to health? How will quantum computing be useful? If you're a specialist in these fields, you can read the original papers or ask nuanced questions in chat and advance your understanding of the latest developments. But these notebooks can also make the complex but important topics understandable to non-specialists or students. Each notebook comes with pre-generated audio and video overviews, flashcards, and other Studio artifacts designed to make the scientific and technological concepts accessible and interesting. And you can always explore the material with our new "Learning Guide" chat mode that effectively gives you a personal tutor to enhance your understanding. There's much more to come on this front, but you can see in these two announcements how we see Notebook as both a workbench for conducting research and a publishing platform for sharing the results of that research once you're ready to make it public. Deep Research is rolling out this week to all users. The first two Google Research notebooks are live now, both of them deep dives into our most recent discoveries involving genetics and health. (Links in the following tweets.) We'll be publishing new notebooks in the series every other week or so for the next few months.
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Two big steps towards our vision for @NotebookLM as the ultimate research platform: • Integrating Deep Research, with a set of only-at-Notebook features that let you explore the retrieved sources • Launching a series of Featured Notebooks curated by Google Research These developments are designed to enhance the full life cycle of research and scholarship: using the power of AI to assemble the knowledge base you need to advance your understanding, and then making your work accessible and intelligible to a wider audience using all the explanatory tools that Notebook offers. If you've used DeepResearch in the Gemini app, you already know that it's a pioneering advance in assembling complex, grounded information on any topic imaginable—collecting an entire trove of material for you and writing a nuanced research report that summarizes the findings. But because NotebookLM is designed to manage and explore potentially hundreds of sources, the Deep Research report is only the beginning of your journey. In our integration, Deep Research gives you an overview all of the sources it found during its research phase, with annotated commentary explaining how each source related to your original query. You can then choose to import some or all of the sources to the notebook, along with the report itself, which you can then explore or transform using the full suite of tools that Notebook offers: grounded chat with citations, Mind Maps, Audio/Video overviews, and much more. And it's that suite of tools that make the Google Research Featured Notebooks so compelling as well. Each notebook contains a curated collection of articles on a specific topic, published by the Google Research team. Think of them as a kind of knowledge base of Google's best thinking on a series of compelling research questions: How do scientists link genetics to health? How will quantum computing be useful? If you're a specialist in these fields, you can read the original papers or ask nuanced questions in chat and advance your understanding of the latest developments. But these notebooks can also make the complex but important topics understandable to non-specialists or students. Each notebook comes with pre-generated audio and video overviews, flashcards, and other Studio artifacts designed to make the scientific and technological concepts accessible and interesting. And you can always explore the material with our new "Learning Guide" chat mode that effectively gives you a personal tutor to enhance your understanding. There's much more to come on this front, but you can see in these two announcements how we see Notebook as both a workbench for conducting research and a publishing platform for sharing the results of that research once you're ready to make it public. Deep Research is rolling out this week to all users. The first two Google Research notebooks are live now, both of them deep dives into our most recent discoveries involving genetics and health. (Links in the following tweets.) We'll be publishing new notebooks in the series every other week or so for the next few months.

Steven Johnson

104,833 просмотров • 9 месяцев назад

Playing Pokemon with LLMs became a benchmark, but what about Super Mario? Turns out Google DeepMind Gemini 2.0 Flash can play Super Mario in real-time due to its fast latency, multimodal input and long context! 🎮 Gemini 2.0 Flash receives screenshots of the game and then generates Python code (PyAutoGUI commands) for each screenshot to control the game for a short period (the next 1 or 2 seconds). Add a while loop to it and you have it play in real-time. 🤯
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Playing Pokemon with LLMs became a benchmark, but what about Super Mario? Turns out Google DeepMind Gemini 2.0 Flash can play Super Mario in real-time due to its fast latency, multimodal input and long context! 🎮 Gemini 2.0 Flash receives screenshots of the game and then generates Python code (PyAutoGUI commands) for each screenshot to control the game for a short period (the next 1 or 2 seconds). Add a while loop to it and you have it play in real-time. 🤯

Philipp Schmid

153,086 просмотров • 1 год назад

New generative UI experiences in the Google Gemini and AI Mode in Search use Gemini 3’s multimodal understanding capabilities to make entire user interfaces for you from scratch — dynamically delivering what you need in a visual and interactive way. Google Research software engineer Yaniv Leviathan shares more about the process of building generative UI (hint: so much fun that the team couldn't stop playing with it) and how he's been using it so far.
1:53

New generative UI experiences in the Google Gemini and AI Mode in Search use Gemini 3’s multimodal understanding capabilities to make entire user interfaces for you from scratch — dynamically delivering what you need in a visual and interactive way. Google Research software engineer Yaniv Leviathan shares more about the process of building generative UI (hint: so much fun that the team couldn't stop playing with it) and how he's been using it so far.

Google

138,435 просмотров • 9 месяцев назад

Got the vibe but not the words? Starting today, you can simply show or tell AI Mode about what you're looking for and get rich, visual results to explore. ✨ This experience works by bringing together Search’s world-class visual understanding from Lens and Image search with Gemini 2.5’s advanced multimodal capabilities. When you show AI Mode what you’re searching for it uses our new “visual search fan-out” technique to get a deeper understanding of what’s in an image (including subtle details and secondary objects), and runs multiple queries in the background. This helps it understand the visual context and nuance of your request to deliver relevant visual responses. You can also tell AI Mode what vibe you’re going for (no image needed). For example, ask AI Mode for bedroom design inspo and go back and forth to refine your vision. We’re rolling out this experience in English in the U.S. starting this week. Try it out and let us know what you think!
0:30

Got the vibe but not the words? Starting today, you can simply show or tell AI Mode about what you're looking for and get rich, visual results to explore. ✨ This experience works by bringing together Search’s world-class visual understanding from Lens and Image search with Gemini 2.5’s advanced multimodal capabilities. When you show AI Mode what you’re searching for it uses our new “visual search fan-out” technique to get a deeper understanding of what’s in an image (including subtle details and secondary objects), and runs multiple queries in the background. This helps it understand the visual context and nuance of your request to deliver relevant visual responses. You can also tell AI Mode what vibe you’re going for (no image needed). For example, ask AI Mode for bedroom design inspo and go back and forth to refine your vision. We’re rolling out this experience in English in the U.S. starting this week. Try it out and let us know what you think!

Google AI

68,285 просмотров • 11 месяцев назад

I built a Claude Code skill that allows it to generate a deep research report over any collection of complex docs (PDFs, Word, Pptx)….and generate word-level citations and bounding boxes directly back to the source! 📝 Check out “/research-docs”. 1. It parses out text and bounding boxes from every doc with liteparse, in seconds. 2. It then generates a full HTML report of the outputs that let you see word-level citations in each page. Raw Claude obviously has deep research capabilities, but it lacks an audit trail back to the source. This skill gives you a researched report that can be audited by others. Check it out: LiteParse:
1:13

I built a Claude Code skill that allows it to generate a deep research report over any collection of complex docs (PDFs, Word, Pptx)….and generate word-level citations and bounding boxes directly back to the source! 📝 Check out “/research-docs”. 1. It parses out text and bounding boxes from every doc with liteparse, in seconds. 2. It then generates a full HTML report of the outputs that let you see word-level citations in each page. Raw Claude obviously has deep research capabilities, but it lacks an audit trail back to the source. This skill gives you a researched report that can be audited by others. Check it out: LiteParse:

Jerry Liu

77,259 просмотров • 4 месяцев назад

(1/5) Gemini 3, our most intelligent model, is landing in Google Search today – starting with AI Mode. Excited that this is the first time we’re shipping a new Gemini model in Search on day one! 🚀 In Search, Gemini 3 with generative layouts will make it easy to get a rich understanding of anything on your mind. It has state-of-the-art reasoning, deep multimodal understanding and advanced agentic capabilities. That allows the model to shine when you ask it to explain advanced concepts or ideas – it reasons and can code interactive visuals in real-time. It can tackle your toughest questions like advanced science.
1:13

(1/5) Gemini 3, our most intelligent model, is landing in Google Search today – starting with AI Mode. Excited that this is the first time we’re shipping a new Gemini model in Search on day one! 🚀 In Search, Gemini 3 with generative layouts will make it easy to get a rich understanding of anything on your mind. It has state-of-the-art reasoning, deep multimodal understanding and advanced agentic capabilities. That allows the model to shine when you ask it to explain advanced concepts or ideas – it reasons and can code interactive visuals in real-time. It can tackle your toughest questions like advanced science.

Robby Stein

94,962 просмотров • 9 месяцев назад

/wayfinder lets you plan your most ambitious projects ever You can give it a destination and it will: - Figure out the frontier of things that can be decided now - Uncover the route ahead as you go - Research, prototype, and discuss with you - Maintain a map in an issue tracker of your choice (GitHub, Linear, etc.) It's revolutionised how I plan coding work - and I'm even using it outside of coding. Get it for free: npx skills add mattpocock/skills Here's the breakdown:
15:08

/wayfinder lets you plan your most ambitious projects ever You can give it a destination and it will: - Figure out the frontier of things that can be decided now - Uncover the route ahead as you go - Research, prototype, and discuss with you - Maintain a map in an issue tracker of your choice (GitHub, Linear, etc.) It's revolutionised how I plan coding work - and I'm even using it outside of coding. Get it for free: npx skills add mattpocock/skills Here's the breakdown:

Matt Pocock

122,823 просмотров • 1 месяц назад

Researchers spend 2 hours daily just scanning papers. What if you can directly find the exact papers you need? Imagine you are working on a research project. ↳ You can go Google Scholar ↳ You hit a search query ↳ Google Scholar return 2000+ papers ↳ You keep opening new papers in new tabs ↳ The scanning process goes on ↳ After an hour, you are still not sure ➤ Which papers are relevant? This is the problem faced by almost all researchers. 🎁 The good news? Meet WisPaper - the perfect paper finder. This tool deploys AI agents to find the exact papers, 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐡𝐨𝐰 𝐖𝐢𝐬𝐏𝐚𝐩𝐞𝐫 𝐰𝐨𝐫𝐤𝐬 1. Go to 2. Enter your search query 3. For example, I entered the following 𝐹𝑖𝑛𝑑 𝑝𝑎𝑝𝑒𝑟𝑠 𝑜𝑛 𝑣𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠 𝑖𝑛 𝑏𝑖𝑔 𝑑𝑎𝑡𝑎 𝑠𝑦𝑠𝑡𝑒𝑚𝑠 4. 𝐖𝐢𝐬𝐏𝐚𝐩𝐞𝐫 will start the search process. 5. The search process is broken down into 3 steps. ✓ Analysing questions ✓ Validating criteria ✓ Searching and validating 6. After completion, you will see a list of papers. 7. You can add papers to your library 8. If you need specific search, you can add criteria. In addition to searching, 𝐖𝐢𝐬𝐏𝐚𝐩𝐞𝐫 also helps in: ➝ Organizing papers in the form of a library ➝ Send you alerts about papers relevant to you 🎗️ Try 𝐖𝐢𝐬𝐏𝐚𝐩𝐞𝐫 today for free:
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Researchers spend 2 hours daily just scanning papers. What if you can directly find the exact papers you need? Imagine you are working on a research project. ↳ You can go Google Scholar ↳ You hit a search query ↳ Google Scholar return 2000+ papers ↳ You keep opening new papers in new tabs ↳ The scanning process goes on ↳ After an hour, you are still not sure ➤ Which papers are relevant? This is the problem faced by almost all researchers. 🎁 The good news? Meet WisPaper - the perfect paper finder. This tool deploys AI agents to find the exact papers, 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐡𝐨𝐰 𝐖𝐢𝐬𝐏𝐚𝐩𝐞𝐫 𝐰𝐨𝐫𝐤𝐬 1. Go to 2. Enter your search query 3. For example, I entered the following 𝐹𝑖𝑛𝑑 𝑝𝑎𝑝𝑒𝑟𝑠 𝑜𝑛 𝑣𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠 𝑖𝑛 𝑏𝑖𝑔 𝑑𝑎𝑡𝑎 𝑠𝑦𝑠𝑡𝑒𝑚𝑠 4. 𝐖𝐢𝐬𝐏𝐚𝐩𝐞𝐫 will start the search process. 5. The search process is broken down into 3 steps. ✓ Analysing questions ✓ Validating criteria ✓ Searching and validating 6. After completion, you will see a list of papers. 7. You can add papers to your library 8. If you need specific search, you can add criteria. In addition to searching, 𝐖𝐢𝐬𝐏𝐚𝐩𝐞𝐫 also helps in: ➝ Organizing papers in the form of a library ➝ Send you alerts about papers relevant to you 🎗️ Try 𝐖𝐢𝐬𝐏𝐚𝐩𝐞𝐫 today for free:

Faheem Ullah

20,271 просмотров • 7 месяцев назад

It takes 5 minutes to create a world-class web research agent that runs 24/7 and returns structured search results. 1. You can limit the sources it's allowed to use 2. You can specify the goals it needs to complete 3. You can design the format structure of the results 4. You can configure how much computing power it will use I show you how to set up a Web Search Agent in the video. A few benefits here: 1. This is great for complex research tasks 2. It's really easy to add this like a LEGO piece to a more complex workflow 3. Create an agent once, and reuse it with different inputs 4. The agent adapts to your use case as you work with it By the way, Nimble published a whole library of templates and research agents you can reuse and modify. You can start sending queries to one of these existing agents, and it will begin to adapt to Go here to try it out: Thanks to the Nimble team for partnering with me on this post.
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It takes 5 minutes to create a world-class web research agent that runs 24/7 and returns structured search results. 1. You can limit the sources it's allowed to use 2. You can specify the goals it needs to complete 3. You can design the format structure of the results 4. You can configure how much computing power it will use I show you how to set up a Web Search Agent in the video. A few benefits here: 1. This is great for complex research tasks 2. It's really easy to add this like a LEGO piece to a more complex workflow 3. Create an agent once, and reuse it with different inputs 4. The agent adapts to your use case as you work with it By the way, Nimble published a whole library of templates and research agents you can reuse and modify. You can start sending queries to one of these existing agents, and it will begin to adapt to Go here to try it out: Thanks to the Nimble team for partnering with me on this post.

Santiago

20,943 просмотров • 27 дней назад