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    Trending in Perplexity

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    2392
    @perplexity_ai

    Our Response to Reddit’s Lawsuit

    Dear Reddit community, You might’ve read Perplexity was named in a lawsuit filed by Reddit this morning. We know companies usually dodge questions during lawsuits, but we’d rather be up front.  Perplexity believes this is a sad example of what happens when public data becomes a big part of a public company’s business model.   Selling access to training data is an increasingly important revenue stream for Reddit, especially now that model makers are cutting back on deals with Reddit or walking away completely. (A trend Reddit has acknowledged in recent earnings reports).  So, why sue Perplexity? Our guess: it’s about a show of force in Reddit’s training data negotiations with Google and OpenAI. (Perplexity doesn’t train foundation models!)  Here’s where we push back. Reddit told the press we ignored them when they asked about licensing. Untrue. Whenever anyone asks us about content licensing, we explain that Perplexity, as an application-layer company, does not train AI models on content. Never has. So it is impossible for us to sign a license agreement to do so.  A year ago, after explaining this, Reddit insisted we pay anyway, despite lawfully accessing Reddit data. Bowing to strong arm tactics just isn’t how we do business.  What does Perplexity actually do with Reddit content? We summarize Reddit discussions, and we cite Reddit threads in answers, just like people share links to posts here all the time. Perplexity invented citations in AI for two reasons: so that you can verify the accuracy of the AI-generated answers, and so you can follow the citation to learn more and expand your journey of curiosity. And that’s what people use Perplexity for: journeys of curiosity and learning.  When they visit Reddit to read your content it’s because they want to read it, and they read more than they would have from a Google search.  Reddit changed its mind this week on whether they want Perplexity users to find your public content on their journeys of learning. Reddit thinks that’s their right. But it is the opposite of an open internet.  In any case, we won’t be extorted, and we won’t help Reddit extort Google, even if they’re our (huge) competitor.  Perplexity will play fair, but we won’t cave. And we won’t let bigger companies use us in shell games.   We’re here to keep helping people pursue wisdom of any kind, cite our sources, and always have more questions than answers. Thanks for reading.

    1238
    @Blobbytheblob101

    Perplexity is DELIBERATELY SCAMMING AND REROUTING users to other models

    As you can see in the graph above, while in October, the use of Claude Sonnet 4.5 Thinking was normal, since the 1st of November, Perplexity has deliberately rerouted most if not ALL Sonnet 4.5 and 4.5 Thinking messages to far worse quality models like Gemini 2 Flash and, interestingly, Claude 4.5 Haiku Thinking which are probably cheaper models. Perplexity is essentially SCAMMING subscribers by marketing their model as "Sonnet 4.5 Thinking" but then having all prompts given by a different model--still a Claude one so we don't realise! Very scummy.

    1170
    @aravind_pplx

    Message from Aravind, Cofounder and CEO of Perplexity

    Hi all - This is Aravind, cofounder and CEO of Perplexity. Many of you’ve had frustrating experiences and lots of questions over the last few weeks. Want to step in and provide some clarity here. Firstly, thanks to all who cared to point out all the product feedback. We will work hard to improve things. Our product and company grew really fast and we now have to uplevel to handle the scale and continue to ship new things while keeping the product reliable. Some explanations below: * **Why Auto mode?** \- All AI products right now are shipping non-stop and adding a ton of buttons and dropdown menus and clutter. Including us. This is not sustainable. The user shouldn't have to learn so much to use a product. That's the motivation with "Auto" mode. Let the AI decide for the user if it's a quick-fast-answer query, or a slightly-slower-multi-step pro-search query, or slow-reasoning-mode query, or a really slow deep research query. The long-term future is that. An AI that decides the amount of compute to apply to a question, and maybe clarify with the user, when not super sure. Our goal isn't to save money and scam you in any way. It's genuinely to build a better product with less clutter and simple selector for customization options for the technically adept and well-informed users.. This is the right long-term convergence point. * **Why are the models inconsistent across modes and why don't I see a model selector on Settings as before?** Not all models apply to every mode. Eg: o3-mini and DeepSeek R1 don't make sense in the context of Pro Search. They are meant to reason and go through chain-of-thought and summarize; while models like Sonnet-3.7 (no thinking mode) or GPT-4o are meant to be really great summarizers with quick-fast-reasoning capabilities (and hence good for Pro searches). If we had the model selector in the same way as before, this just leads to more confusion as to which model to pick for what mode. As for Deep Research, it's a combination of multiple models that all work together right now: 4o, Sonnet, R1, Sonar. There's absolutely nothing to control there, and hence, why no model choice offered. * **How does the new model selector work?** Auto doesn't need you to pick anything. Pro is customizable. Pro will persist across follow-ups. Reasoning does not, but we intend to merge Pro and Reasoning into one single mode, where if you pick R1/o3-mini, chain-of-thought will automatically apply. Deep Research will remain its own separate thing. The purpose of Auto is to route your query to the best model for the given task. It’s far from perfect today but our aim is to make it so good that you don’t have to keep up with the latest 4o, 3.7, r1, etc. * **Infra Challenges**: We're working on a new more powerful deep research agent that thinks for 30 mins or more, and will be the best research agent out there. This includes building some of the tool use and interactive and code-execution capabilities that some recent prototypes like Manus have shown. We need a rewrite of our infrastructure to do this at scale. This meant transitioning the way we do our logging and lookups, and removing code written Python and rewriting it in GoLang. This is causing us some challenges we didn't foresee on the core product. You the user shouldn't ideally even need to worry about all this. Our fault. We are going to deprioritize shipping new features at the pace we normally do and just invest into a stable infrastructure that will maximize long-term velocity over short-term quick ships. * **Why does Deep Research and Reasoning go back to Auto for follow-ups?** \- Few months ago, we asked ourselves “What stops users from asking follow-up questions?” Given we can’t ask each of you individually, we looked at the data and saw that 15-20% of Deep Research queries are not seen at all bc they take too long; many users ask simple follow-ups. As a result, this was our attempt at making follow-ups fast and convenient. We realize many of you want continued Reasoning mode for your work, so we’re planning to make those models sticky. To do this, we’ll combine the Pro + Reasoning models as “Pro”, which will be sticky and not default to Auto. * **Why no GPT-4.5?** \- This is an easier one. The decoding speed for GPT-4.5 is only 11 tokens/sec (for comparison, 4o does 110 tokens/sec (10x faster) and our own Sonar model does [1200 tokens/sec](https://www.perplexity.ai/hub/blog/meet-new-sonar) (100x faster)). This led to a subpar experience for our users who expect fast, accurate answers. Until we can achieve speeds similar to what users expect, we will have to hold off on providing access to this model. * **Why are there so many UI bugs & things missing/reappearing?** \- We’re always working to improve the answer experience with redesigns, like the new [Answer mode](https://x.com/AravSrinivas/status/1904571071250260110). In the spirit of shipping so much code and launching quickly, we’ve missed the mark on quality, leading to various bugs and confusion for users. We’re unapologetic in trying new things for our users, but do apologize for the recent dip in quality and lack of transparency (more on that below). We’re implementing stronger processes to improve our quality going forward. * **Are we running out of funding and facing market pressure to IPO**? No. We have all the funding we've raised, and our revenue is only growing. The objective behind Auto mode is to make the product better, not to save costs. If anything, I have learned it's better to communicate more transparently to avoid the any incorrect conclusions. Re IPO: We have no plans of IPOing before 2028. The above is not a comprehensive response to all of your concerns and questions but a signal that we hear you and we’re working to improve. It’s exciting and truly a privilege to have you all on this journey to build the best answer engine.  Lastly, to provide more transparency and insight into what we’re working on, I’ll be planning on hosting an AMA on Reddit in April to answer more of your questions. Please keep an eye out for a follow-up announcement on that! Until next time, Aravind Srinivas & the Perplexity team

    893
    @witcherisdamned

    Perplexity is unhinged

    Perplexity is unhinged

    867
    @utilitymro

    AMA with Perplexity's Aravind Srinivas, Denis Yarats, Tony Wu, Tyler Tates, and Weihua Hu (Perplexity Labs)

    Today, we're hosting an AMA to answer your questions around Perplexity Labs! **Our hosts** * Aravind Srinivas (co-founder & CEO) (u/aravind\_pplx) * Denis Yarats (co-founder & CTO) (u/denis-pplx) * Tony Wu (VP of Engineering) (u/Tony-Perplexity) * Tyler Tates (Product) (u/tylertate) * Weihua Hu (Member of Technical Staff) (u/weihua916) **Ask us anything about** * The process of building Labs (challenges, fun parts) * Early user reactions to Labs * Most popular use-cases of Perplexity Labs * How they envision Labs getting better * How knowledge work will evolve over the next 5-10 years * What is next for Perplexity * How Labs and Comet fit together * What else is on your mind *(be constructive and respectful)* **When does it start?** We will be starting at 10am PT and will from 10:00am to 11:30am PT! Please submit your questions below! **What is Perplexity Labs?** [Perplexity Labs](https://www.perplexity.ai/hub/blog/introducing-perplexity-labs) is a way to bring your projects to life by combining extensive research and analysis with report, spreadsheet, and dashboard generating capabilities. Labs will understand your question and use a suite of tools like web browsing, code execution, and chart and image creation to turn your ideas into entire apps and analysis. >Hi all - thanks all for a great AMA! > >We hope to see you soon and please help us make Labs even better!

    Featured Prompts

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    Perplexity Directory

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    Learn

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    Top Videos

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    Perplexity AI: Complete Beginner Tutorial 2025

    Jeff Su

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    Rules

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    AI/ML

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    The AGENT Prompt Framework for Perplexity: 1. Ask — Clearly define the task with specifics 2. Ground — Provide context, constraints, and background 3. Example — Show desired output format 4. Narrow — Set boundaries and exclusions 5. Test — Verify with follow-up questions Covers how to leverage Skills and Custom Instructions as force multipliers for the Perplexity Computer agent.
    Extracted Perplexity internal system prompt revealing how the platform instructs its models. Includes citation formatting rules, search result interpretation guidelines, source ranking preferences, and response structure directives. Useful as reference for understanding Perplexity's default behavior.
    Step-by-step tutorial for creating Space instructions: 1. Define persona and tone (formal, casual, expert) 2. Set knowledge boundaries (topics, sources, recency) 3. Configure response guidelines (length, format, citations) 4. Add prohibited content sections (hallucination guard) 5. Include example interactions for consistency Each section explained with copy-paste templates and real-world examples.

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    You are an AI assistant with access to real-time web search tools. Your goal is to provide fast, accurate answers using web search when needed. Rules: 1. Make at most ONE tool call before responding. 2. Use keyword-based search queries (2-5 words optimal). 3. Provide only the final answer. Do not include explanation of your reasoning steps. 4. If the answer should be a brief sentence, write it directly. 5. If the answer should be a list, write a natural introduction followed by the list. 6. Cite sources inline using [1], [2] format immediately after claims. 7. Do not mention that you searched or used tools. Present information naturally. 8. Prioritize speed and precision over comprehensiveness.
    You are a helpful AI assistant with access to web search and URL fetching tools. Provide accurate, well-researched answers. Rules: 1. You MUST make at least one tool call before answering. Never answer without searching first. 2. You may make up to three tool calls maximum to research the query. 3. Use web_search for finding information and fetch_url for reading specific pages in detail. 4. Provide comprehensive responses with inline citations using [1][2] format. 5. Do not include explanation of intermediate reasoning steps. 6. Structure responses with headers, bullet points, or tables when appropriate. 7. If sources conflict, note the discrepancy and cite both sources. 8. Always prefer primary sources (official docs, research papers) over secondary reporting.
    You are an expert research analyst with access to web search and URL fetching tools. Conduct thorough, multi-step research to provide comprehensive, institutional-grade analysis. Rules: 1. Conduct iterative research across multiple searches (up to 10 steps). 2. For complex queries, aim to consult 50-80+ sources. 3. Cross-validate claims across multiple independent sources. 4. Write at domain-expert level with precise terminology. 5. Calibrate response length to query complexity: simple factual queries get concise answers, complex analytical queries get detailed reports. 6. Prioritize primary sources: official documentation, research papers, government data, company filings. 7. Structure output with clear headers, tables for comparisons, and inline citations [1][2][3]. 8. If information is uncertain or conflicting, explicitly state the uncertainty and present all perspectives. 9. Include a brief methodology note explaining your research approach. 10. Flag any areas where additional research would be valuable.

    Security

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    # Cybersecurity Threat Intelligence Space ## Role You are a cybersecurity threat intelligence analyst monitoring and researching security threats. ## Research Protocols ### Vulnerability Research - Search CVE databases (NVD, MITRE) for relevant vulnerabilities - Check exploit-db and GitHub for proof-of-concept exploits - Assess CVSS scores and real-world exploitability - Identify affected software versions and available patches - Research vendor response timeline and patch quality ### Threat Actor Research - Search for threat actor profiles and TTPs (MITRE ATT&CK mapping) - Recent campaign activity and target sectors - Known indicators of compromise (IOCs) - Attribution confidence level ### Incident Analysis - Timeline reconstruction from available sources - Attack vector and initial access method - Impact assessment (data exposure, business disruption) - Lessons learned and defensive recommendations - Similar past incidents for comparison ### Security Posture Assessment - Search for common vulnerabilities in specified technology stack - Review security best practices from NIST, CIS, OWASP - Identify configuration hardening opportunities - Assess compliance requirements (PCI, HIPAA, SOC 2) ## Source Priorities 1. CISA alerts and advisories 2. Vendor security bulletins 3. MITRE ATT&CK and CVE databases 4. Security researcher blogs (Krebs, Schneier, Project Zero) 5. Threat intelligence reports (Mandiant, CrowdStrike, Recorded Future) ## Rules - Never share or help create exploit code - Always recommend coordinated disclosure - Classify information sensitivity (TLP: WHITE/GREEN/AMBER/RED) - Include remediation priority based on risk

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