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Introducing "gap analysis"—new addition to scira's "extreme" mode! how it works—after the research plan is executed—the LLM will run a gap analysis to find the gaps in the results of the research plan and then search for the gaps and fill the information—and finally run a final synthesis or...

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Scira AI

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15,643 просмотров • 1 год назад •via X (Twitter)

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Nigam Arora1 год назад

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PhD Students – How to identify trends in research? First, let’s understand why trends are important. Identifying trends in research can help you ➝ To see if a research area is active or saturated ➝ To find research gaps ➝ To understand how a research field has evolved ➝ To identify emerging R&D opportunities 𝐇𝐨𝐰 𝐭𝐨 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐭𝐫𝐞𝐧𝐝𝐬 𝐢𝐧 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡? Very simple. Use the following 3 steps. 1. Go to 2. Enter your question or research area 3. @PatsnapEurekaIP provide you with trend analysis. The trend analysis contains the following ✓ 𝐑𝐞𝐜𝐞𝐧𝐜𝐲: The number of papers in the past 3 years ✓ 𝐈𝐧𝐟𝐥𝐮𝐞𝐧𝐜𝐞: The number of citations in the past 3 years ✓ 𝐓𝐫𝐞𝐧𝐝: Trend of papers in the past 10 years Eureka shows trends analysis for the following ↳ Research papers ↳ Patents ↳ News articles Find research trends in seconds with Eureka. — A free AI powered search engine for research — It helps researchers to track research trends — It uses insights from 210M+ research papers Try Eureka for FREE
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PhD Students – How to identify trends in research? First, let’s understand why trends are important. Identifying trends in research can help you ➝ To see if a research area is active or saturated ➝ To find research gaps ➝ To understand how a research field has evolved ➝ To identify emerging R&D opportunities 𝐇𝐨𝐰 𝐭𝐨 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐭𝐫𝐞𝐧𝐝𝐬 𝐢𝐧 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡? Very simple. Use the following 3 steps. 1. Go to 2. Enter your question or research area 3. @PatsnapEurekaIP provide you with trend analysis. The trend analysis contains the following ✓ 𝐑𝐞𝐜𝐞𝐧𝐜𝐲: The number of papers in the past 3 years ✓ 𝐈𝐧𝐟𝐥𝐮𝐞𝐧𝐜𝐞: The number of citations in the past 3 years ✓ 𝐓𝐫𝐞𝐧𝐝: Trend of papers in the past 10 years Eureka shows trends analysis for the following ↳ Research papers ↳ Patents ↳ News articles Find research trends in seconds with Eureka. — A free AI powered search engine for research — It helps researchers to track research trends — It uses insights from 210M+ research papers Try Eureka for FREE

Faheem Ullah

73,369 просмотров • 6 месяцев назад

$ETH formed a gap over the weekend. Will it fill this gap first, or will it move higher to fill the upper gaps instead? What do you think? 👀
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$ETH formed a gap over the weekend. Will it fill this gap first, or will it move higher to fill the upper gaps instead? What do you think? 👀

CryptoJack

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Today, Search Live is available in English in the U.S. — no Labs opt-in required. ✨ You can now have a free-flowing, back-and-forth conversation with Search using your voice and phone camera. Live in Search combines the power of Google Lens and AI Mode to see and accurately identify objects in front of you. This allows Search to understand exactly what you are looking at and help you find the most relevant information across the web to respond to your questions in real time. Try it today by tapping Live in the Google app (iOS and Android) or Lens, and let us know what you think!
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Today, Search Live is available in English in the U.S. — no Labs opt-in required. ✨ You can now have a free-flowing, back-and-forth conversation with Search using your voice and phone camera. Live in Search combines the power of Google Lens and AI Mode to see and accurately identify objects in front of you. This allows Search to understand exactly what you are looking at and help you find the most relevant information across the web to respond to your questions in real time. Try it today by tapping Live in the Google app (iOS and Android) or Lens, and let us know what you think!

Google AI

48,572 просмотров • 10 месяцев назад

PhD Students – How to identify a research gap in seconds? Use this FREE tool to identify research gap in seconds. 1. Go to 2. Click on 𝑇𝑜𝑝𝑖𝑐 𝐸𝑥𝑝𝑙𝑜𝑟𝑒𝑟 ---> 𝐶𝑟𝑒𝑎𝑡𝑖𝑣𝑒 𝑇𝑜𝑜𝑙 3. Enter your topic e.g., deep learning for healthcare 4. Now click on 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒 𝑖𝑑𝑒𝑎𝑠 5. ResearchCollab will give you the following 7 gaps ➝ Research that combines multiple existing research ➝ Research that adapts a new approach/technology ➝ Research that modifies existing variables in research ➝ Research that incorporates new variables ➝ Research that eliminates variables from research ➝ Research that re-arrange existing variables Pick the gap you think has the most potential. Then click on 𝑡𝑜𝑝𝑖𝑐 𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐂𝐨𝐥𝐥𝐚𝐛 will dive deeper into the selected topic It gives you different angles of the chosen research gap. It is very helpful and at the same time very easy to use Try 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐂𝐨𝐥𝐥𝐚𝐛 today:
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PhD Students – How to identify a research gap in seconds? Use this FREE tool to identify research gap in seconds. 1. Go to 2. Click on 𝑇𝑜𝑝𝑖𝑐 𝐸𝑥𝑝𝑙𝑜𝑟𝑒𝑟 ---> 𝐶𝑟𝑒𝑎𝑡𝑖𝑣𝑒 𝑇𝑜𝑜𝑙 3. Enter your topic e.g., deep learning for healthcare 4. Now click on 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒 𝑖𝑑𝑒𝑎𝑠 5. ResearchCollab will give you the following 7 gaps ➝ Research that combines multiple existing research ➝ Research that adapts a new approach/technology ➝ Research that modifies existing variables in research ➝ Research that incorporates new variables ➝ Research that eliminates variables from research ➝ Research that re-arrange existing variables Pick the gap you think has the most potential. Then click on 𝑡𝑜𝑝𝑖𝑐 𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐂𝐨𝐥𝐥𝐚𝐛 will dive deeper into the selected topic It gives you different angles of the chosen research gap. It is very helpful and at the same time very easy to use Try 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐂𝐨𝐥𝐥𝐚𝐛 today:

Faheem Ullah

106,224 просмотров • 5 месяцев назад

Web search is now enabled by default for the Codex CLI and IDE Extension 🎉 By default it will use a web search cache but you can toggle live results or if you use --yolo live results are enabled by default. More details in the changelog 👇
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Web search is now enabled by default for the Codex CLI and IDE Extension 🎉 By default it will use a web search cache but you can toggle live results or if you use --yolo live results are enabled by default. More details in the changelog 👇

dominik kundel

265,718 просмотров • 6 месяцев назад

You can create a Grok Project, have it search for relevant research papers, and attach the good ones to your project. Because Grok 4 can parse PDFs, you can interact with the model, and it will have direct knowledge of your research.
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You can create a Grok Project, have it search for relevant research papers, and attach the good ones to your project. Because Grok 4 can parse PDFs, you can interact with the model, and it will have direct knowledge of your research.

tetsuo

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Introducing /last30days v3.1! Now with --competitors search. Intelligent vs. mode where you just say ANYTHING and say "/last30days OpenAI --competitors and it does an intelligent search to pick the top 2 competitors and run a vs. mode between the three. Now up to ⭐23k stars on github!
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Introducing /last30days v3.1! Now with --competitors search. Intelligent vs. mode where you just say ANYTHING and say "/last30days OpenAI --competitors and it does an intelligent search to pick the top 2 competitors and run a vs. mode between the three. Now up to ⭐23k stars on github!

Matt Van Horn

11,794 просмотров • 3 месяцев назад

My submission to the Cerebras Google Gemma hackathon, Carbide. Carbide is an ontology builder. Based on a research prompt Carbide uses Cerebras Gemma4 agents to build an ontology. The agents connects individual nodes of information based on the relationship that the agents infer. The agents also assign a confidence metric for how accurate they think the relationship is. You can see each node in a relationship table of the information -> relationship -> target -> and the confidence the relationship is true. The inspector shows the node's connected edges and as an expand button to grow the ontology in the direction of that information. You can scroll through each concept and load them in the inspector. You can search the entire graph based on the kind of relationships generated, or key words. A visual shows how each nodes connects to another one and by what relationship, and changes with new nodes or search filters. Sorry for the audio quality. $CBRS $GOOG
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My submission to the Cerebras Google Gemma hackathon, Carbide. Carbide is an ontology builder. Based on a research prompt Carbide uses Cerebras Gemma4 agents to build an ontology. The agents connects individual nodes of information based on the relationship that the agents infer. The agents also assign a confidence metric for how accurate they think the relationship is. You can see each node in a relationship table of the information -> relationship -> target -> and the confidence the relationship is true. The inspector shows the node's connected edges and as an expand button to grow the ontology in the direction of that information. You can scroll through each concept and load them in the inspector. You can search the entire graph based on the kind of relationships generated, or key words. A visual shows how each nodes connects to another one and by what relationship, and changes with new nodes or search filters. Sorry for the audio quality. $CBRS $GOOG

QuantHood

16,376 просмотров • 1 месяц назад

The upgraded Google app for desktop is now available for Windows users globally in English. 💻✨ Use a simple keyboard shortcut (Alt + Space) to instantly find what you need—information from the web, your computer files, installed apps, and Google Drive files—all from the Search box. With AI Mode in Search and Lens built right in, you can ask whatever’s on your mind or search what’s on your screen to get helpful AI-powered responses with links to the web.
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The upgraded Google app for desktop is now available for Windows users globally in English. 💻✨ Use a simple keyboard shortcut (Alt + Space) to instantly find what you need—information from the web, your computer files, installed apps, and Google Drive files—all from the Search box. With AI Mode in Search and Lens built right in, you can ask whatever’s on your mind or search what’s on your screen to get helpful AI-powered responses with links to the web.

Rajan Patel

114,317 просмотров • 4 месяцев назад

Your app can now search the web for images. Web search in the Responses API now supports image results in addition to text results, so you can build apps that surface products, places, visual references, and source links for inspiration.
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Your app can now search the web for images. Web search in the Responses API now supports image results in addition to text results, so you can build apps that surface products, places, visual references, and source links for inspiration.

OpenAI Developers

155,120 просмотров • 2 месяцев назад

New in Claude Code: /ultraplan Claude builds an implementation plan for you on the web. You can read it and edit it, then run the plan on the web or back in your terminal. Available now in preview for all users with CC on the web enabled.
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New in Claude Code: /ultraplan Claude builds an implementation plan for you on the web. You can read it and edit it, then run the plan on the web or back in your terminal. Available now in preview for all users with CC on the web enabled.

Thariq

1,300,439 просмотров • 4 месяцев назад

Introducing Jan-v1: 4B model for web search, an open-source alternative to Perplexity Pro. In our evals, Jan v1 delivers 91% SimpleQA accuracy, slightly outperforming Perplexity Pro while running fully locally. Use cases: - Web search - Deep Research Built on the new version of Qwen's Qwen3-4B-Thinking (up to 256k context length), fine-tuned for reasoning and tool use in Jan. You can run the model in Jan, llama.cpp, or vLLM. To enable search in Jan, go to Settings → Experimental Features → On, then Settings → MCP Servers → enable a search-related MCP such as Serper. Use the model: - Jan-v1-4B: - Jan-v1-4B-GGUF: Credit to the Qwen team for Qwen3 4B Thinking & Georgi Gerganov for llama.cpp.
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Introducing Jan-v1: 4B model for web search, an open-source alternative to Perplexity Pro. In our evals, Jan v1 delivers 91% SimpleQA accuracy, slightly outperforming Perplexity Pro while running fully locally. Use cases: - Web search - Deep Research Built on the new version of Qwen's Qwen3-4B-Thinking (up to 256k context length), fine-tuned for reasoning and tool use in Jan. You can run the model in Jan, llama.cpp, or vLLM. To enable search in Jan, go to Settings → Experimental Features → On, then Settings → MCP Servers → enable a search-related MCP such as Serper. Use the model: - Jan-v1-4B: - Jan-v1-4B-GGUF: Credit to the Qwen team for Qwen3 4B Thinking & Georgi Gerganov for llama.cpp.

👋 Jan

690,311 просмотров • 1 год назад

📍A local SEO trick every pro should know: Use the UULE parameter to set a precise location for your search results. Adding a UULE parameter to your Google search query lets you see search results from a specific region without needing an extension or being physically there. Here’s how: 1️⃣ Generate a UULE for the location (Find a tool by googling “uule generator”) You will get a code that looks like this: &uule=w+CAIQICIQRWRtb250b24sIENhbmFkYQ 2️⃣ Now run any search in Google. 3️⃣ Look at the URL for your search and replace the last part of it with the UULE parameter. For example: google . com/search?q=sushi+restaurant &uule=w+CAIQICIQRWRtb250b24sIENhbmFkYQ How do you set your search location? #seo #localseo
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📍A local SEO trick every pro should know: Use the UULE parameter to set a precise location for your search results. Adding a UULE parameter to your Google search query lets you see search results from a specific region without needing an extension or being physically there. Here’s how: 1️⃣ Generate a UULE for the location (Find a tool by googling “uule generator”) You will get a code that looks like this: &uule=w+CAIQICIQRWRtb250b24sIENhbmFkYQ 2️⃣ Now run any search in Google. 3️⃣ Look at the URL for your search and replace the last part of it with the UULE parameter. For example: google . com/search?q=sushi+restaurant &uule=w+CAIQICIQRWRtb250b24sIENhbmFkYQ How do you set your search location? #seo #localseo

Darren Shaw

28,511 просмотров • 2 лет 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 • 11 дней назад

Introducing /search - the simplest way for agents and devs to discover the web 👀 Our #1 most requested endpoint is finally here. Search the web AND scrape all results in an LLM-ready format with one API call. Now live on our API, MCP, and all integrations 🔥
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Introducing /search - the simplest way for agents and devs to discover the web 👀 Our #1 most requested endpoint is finally here. Search the web AND scrape all results in an LLM-ready format with one API call. Now live on our API, MCP, and all integrations 🔥

Firecrawl

390,772 просмотров • 1 год назад

RLM is the most import foundation of my Pi Harness (other than Pi of course). It's seeded with late interaction retrieval results (thanks to @lightonai for pylate). The Agent initiates it with query then.. 𝐒𝐞𝐭𝐮𝐩 A python REPL is created and seeded with: 1. Late interaction search to pre-filter. Instead of doing top 3/5/10, it's top hundreds of documents. This is set into a `context` variable. 2. Python functions are loaded in to do more searches if `context` variable isn't enough. And to make llm calls with cheaper models in parallel batches. 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐨𝐨𝐩 From there, an LLM iterates in the REPL based on the query. It's just like exploring in a jupyter notebook. The LLM writes prose (like a markdown cell) and code to be run in the REPL each turn. This allows the LLM to sort, filter, and synthesize information. It can fan out and ask smaller models to summarize, combine, contrast, or do anything else to documents to help it understand the data. After several turns the LLM reponds with the final answer. Either because it found the answer, or hit the budget limit. Context as a Python variable, LLM as the programmer, REPL as the runtime. 𝐖𝐡𝐲 𝐃𝐨𝐞𝐬 𝐓𝐡𝐢𝐬 𝐖𝐨𝐫𝐤 1. Richer Shell. Agents (and subagents) work by intermixing code and prose/thinking. But they use static scripts or bash that run and exit and start over each tool call. That's not ideal for exploration and synthesis of data. For that, state is useful to continue building and exploring the data as you learn more. There's a reason jupyter notebooks have been popular with data scientists. 2. Keeps main agent context clean. The better context you have the better the agent will perform (duh!). This means three thing: better human input, less missing search results, and less incorrect search results. Letting the agent iterate allows it to synthesize just what is needed and nothing else. All bad paths or peeks at something that turns out to be irrelevant stays out of main agent context. 3. Stack the good ideas! People often compare late interaction search vs RLM. Or static vs dynamic languages. Or agentic search vs semantic search. But...You can just use them all together for what they're each good at. Use them all for the area they're really great for. Read the full post which has more detail about how and why.
3:43

RLM is the most import foundation of my Pi Harness (other than Pi of course). It's seeded with late interaction retrieval results (thanks to @lightonai for pylate). The Agent initiates it with query then.. 𝐒𝐞𝐭𝐮𝐩 A python REPL is created and seeded with: 1. Late interaction search to pre-filter. Instead of doing top 3/5/10, it's top hundreds of documents. This is set into a `context` variable. 2. Python functions are loaded in to do more searches if `context` variable isn't enough. And to make llm calls with cheaper models in parallel batches. 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐨𝐨𝐩 From there, an LLM iterates in the REPL based on the query. It's just like exploring in a jupyter notebook. The LLM writes prose (like a markdown cell) and code to be run in the REPL each turn. This allows the LLM to sort, filter, and synthesize information. It can fan out and ask smaller models to summarize, combine, contrast, or do anything else to documents to help it understand the data. After several turns the LLM reponds with the final answer. Either because it found the answer, or hit the budget limit. Context as a Python variable, LLM as the programmer, REPL as the runtime. 𝐖𝐡𝐲 𝐃𝐨𝐞𝐬 𝐓𝐡𝐢𝐬 𝐖𝐨𝐫𝐤 1. Richer Shell. Agents (and subagents) work by intermixing code and prose/thinking. But they use static scripts or bash that run and exit and start over each tool call. That's not ideal for exploration and synthesis of data. For that, state is useful to continue building and exploring the data as you learn more. There's a reason jupyter notebooks have been popular with data scientists. 2. Keeps main agent context clean. The better context you have the better the agent will perform (duh!). This means three thing: better human input, less missing search results, and less incorrect search results. Letting the agent iterate allows it to synthesize just what is needed and nothing else. All bad paths or peeks at something that turns out to be irrelevant stays out of main agent context. 3. Stack the good ideas! People often compare late interaction search vs RLM. Or static vs dynamic languages. Or agentic search vs semantic search. But...You can just use them all together for what they're each good at. Use them all for the area they're really great for. Read the full post which has more detail about how and why.

Isaac Flath

40,212 просмотров • 3 месяцев назад

FireCrawl just launched Fire Enrich. You can now upload a CSV with emails, and the AI agents will search the web to automatically fill in any missing data you need. Here’s how:
0:20

FireCrawl just launched Fire Enrich. You can now upload a CSV with emails, and the AI agents will search the web to automatically fill in any missing data you need. Here’s how:

Alvaro Cintas

34,586 просмотров • 1 год назад