Perplexity

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

What was ChatGPT secretly doing on my computer?

No request running overnight, yet 61 Gb, my computer only has 24 RAM, so it probably went digging into the SSD. Should I be concerned? Anyone got that?

Built a free MCP for tracking which URLs Claude (and 5 other engines) cite for any query

We were comparing hosted AI citation dashboards (Profound, AthenaHQ, Otterly) and they all start at $295 to $499 a month. The data they collect is mostly the same data you can pull from each vendor's API. So we built an MCP server that does the same job locally. Citation Intelligence is a stdio MCP server with 12 tools that track what Claude, ChatGPT, Perplexity, Gemini, Google AI Overviews, and Bing cite for any query. Install: `npx -y` u/automatelab`/citation-intelligence` Add to `.mcp.json`: { "mcpServers": { "citation-intelligence": { "command": "npx", "args": ["-y", "@automatelab/citation-intelligence"] } } } Three of the tools run on a local cache and cost zero. The rest are bring-your-own-keys (ANTHROPIC\_API\_KEY, OPENAI\_API\_KEY, GEMINI\_API\_KEY, SERPAPI\_API\_KEY), about $0.01 to $0.03 per query. The one that actually changed our editorial flow is `gsc_citation_gap` \- it joins Google Search Console data with AI citation status and surfaces pages that rank in Google but are not cited by any AI engine. Those pages are the editorial budget. Repo and full tool list: [https://github.com/automatelab/citation-intelligence](https://github.com/automatelab/citation-intelligence) Launch write-up: [https://automatelab.tech/launching-the-citation-intelligence-mcp/](https://automatelab.tech/launching-the-citation-intelligence-mcp/) Curious if anyone else here is tracking AI citations in their agent loop rather than in a dashboard, and how you handle the predict-vs-measure tradeoff.

Why We Build

One silver-lining to the dead internet we're living in, today, is that it's very quickly teaching us that we can't rely on our senses as much as we believe we can. It's not healthy to always live in skepticism, but it is necessary in a World where you don't know what's up or down anymore. That's why we need great minds to focus their attention on solving the problems associated with credible information sharing without it becoming some centralized playground designed to look like the free-flowing exchange of ideas. If we don't solve for that, then I guess we're heading into a future that a small handful of people want because elections or public opinion will no longer matter. One of the biggest focuses in AI should be in figuring out how to get it to provide deep credible knowledge in specific domains that can be best applied to the problems we're trying to solve. Sure, it can do this with enough fenagling, but what I really mean is having something easy for everyone to use like Perplexity or Gemini, only it doesn't simply find consensus information from the internet using all these black box methods that are owned by major corporations. Instead, it should use direct knowledge from domain experts who structure and cite their material and as users, we should be able to backtrack all of it, including the original author. And all of this should be achievable by simply engaging with a chatbot agent that can reliably go out and help me discover all of these things. Also, we shouldn't have to simply trust that the application works. We should be able to go in and see exactly how it's working. This way, the public can audit the systems we're relying on for grounding our worldviews. That, to me, is where we should be if we really want to break from the chains of propaganda and reclaim our genuine thoughts about how we ought to live. The alternative independent media space was co-opted long ago and now all of the feeds keep us in a state of perpetual dislocation from our friends, family, communities, new solutions, and better approximations to the truth. We exist in a walled-off digital pasture. But if regular people who are smart and capable enough decide to leverage this new technology, then we can break through the fencing and finally live in a world where discovery-based researching and learning can be easier than Google, which could eventually individuate society again, like how it was before, instead of keeping us clustered into specific groups based on our viewing preferences. That's why my brother and I got into this business. Yeah, sure, we also wanna make a buck so we can retire with dignity. That's true. But the drive has always stemmed from wanting to figure out a better way for people to share hidden insights and create things that are bigger than they thought they could handle. We have a long way to go, but we're making the first small steps, even if it isn't obvious, just yet. Bottom line, though? Humanity must figure out a way to help us master the means and methods of discovery-based knowledge acquisition, execution, and immediate distribution of information based on relevancy and needs from those who search instead of those who passively soak information in from the curated feeds. And all of this needs to be easy enough for a 12 year-old to do. If anyone else is working on this problem, we'd love to hear your thoughts, even if it's through a DM. We're living in the most exciting times, but with adventure, comes danger. So maybe, idk. Let's make it more fun and less hazardous, so that we can, at least, live long enough to re-tell this great story that we're all a part of.

Why doesn't claude recognize when a file it's commenting on/writing to is out of date?

I have been programming a lot time, but now it's hard to remember what life was like before I could just prompt "Build GTA7. Make no mistakes." Right now, I'm learning rust and bevy and since I'm trying to learn, I mostly only query claude to figure out what I'm doing wrong and how to write more idiomatic rust code. Problems arise when I ask claude to read the code, I respond to feedback, and ask claude something again and it repeats the advice from earlier even though this is no longer representative of the code. This happens on every project, but especially this one since claude is unaware of when I make changes and I'm doing all the changes. So every prompt begins with "re-read the code." In other projects, I have to prod claude to always check \`git diff\` so that it actually understands the change under discussion instead of treating all code as new. Sometimes I add this to [CLAUDE.md](http://CLAUDE.md), but it's surprising to me that claude doesn't do it automatically. I feel like a smarter AI client would always check the modified time and refresh its understanding of the code if the modified time is more recent than the last prompt. Even better, it could copy the code to a temp file and when it detects mtime is more recent than the last prompt, do a diff of the temp file with the new file and inform claude of the specific lines that changed. But to my awareness prompts are not properly timestamped. I really don't care when claude fails to implement something correctly, I mostly just get frustrated with myself for either being unable to communicate with the robot or having relied on it in the first place, but for a robot whose job it is to maintain code, it's rather perplexing to me that it doesn't check if the file has been modified since last it checked. This burns a lot of tokens because it will try to do an edit, fail, reread the file, and then edit again, wasting a lot of tokens. And I don't want it rereading much of the file either unless the relevant pieces of code are what changed.

I created an amazing Chrome extension that helps transfer chats to another AI when the chat limit is reached.

I created a chrome extension which helps in switching conversation without losing your Chat context between multiple AI , such as Chatgpt to Gemini , claude , grok , etc . You can interchange btw any of them . Try it's free - https://chromewebstore.google.com/detail/ai-chat-transfer/gfeohkmgfphhoodfhiaffmgcoeljhnhp Uses of this extension - The extension is useful when chat limits, usage caps, or context limits are reached on one platform. Instead of losing progress or restarting from scratch, users can continue the same conversation in another AI tool while keeping important context intact. It is designed for researchers, developers, writers, students, marketers, creators, and AI power users who regularly work across multiple AI models. The extension helps preserve prompts, code snippets, brainstorming sessions, research discussions, and long-form conversations. AI CHAT TRANSFER also helps reduce repetitive explaining by carrying over previous discussion context between AI systems. This makes comparing responses, testing different models, and maintaining workflow continuity much faster and more efficient.

shipped early access of my Mac overlay built with Claude Code, looking for people to try it

Hello everyone. Built this because I was sending 50+ prompts a day across Claude, ChatGPT, Perplexity and re-explaining my entire project every single time I opened a fresh chat. Got tired enough of it to build a fix. It's a Mac overlay that sits on top of whichever AI tool you're in and modifies the prompt before it gets sent. Two layers under the hood: a contextual agent that classifies your query and pulls relevant chunks from your vault, and a prompt architect that rewrites your raw input into something clean and properly structured. So you type something messy and what actually reaches the model is a better version of what you meant to ask. The vault uses a GraphRAG setup so the retrieval is semantic, not just keyword matching. Built the whole thing with Claude Code over the past few months as an industrial engineering student with no Mac dev background. Weirdly meta experience using Claude Code to make Claude usage cleaner. Right now I'm focused on improving the classification and the prompt rewriting layer. It's not perfect but it works well enough that I use it every day myself. Looking for people who juggle multiple AI tools and want to try it. Early access is free at getlumia.ca. Any feedback on the architecture or how it feels to use would genuinely help.

Rho cut weekly meeting time by 90% with Perplexity Computer. Computer checks Slack, Notion, Jira, Figma, and Google Docs, then flags missing tasks and changes the team needs to see. 120 work hours s

Rho cut weekly meeting time by 90% with Perplexity Computer. Computer checks Slack, Notion, Jira, Figma, and Google Docs, then flags missing tasks and changes the team needs to see. 120 work hours saved during a 12-week project. Read the customer story: https://t.co/QfuQV6k6cj

shipped my first chrome extension this week, came out of pure frustration tbh

been using AI tools nonstop for work and kept noticing my sessions would just... degrade. like the answers would get worse over time in the same chat and i had no idea why. turns out context windows are a thing and after a while the AI literally starts forgetting what you told it at the start so i spent a few weeks building something dumb and simple. it's just a little pill that floats on claude, chatgpt, gemini and perplexity and shows you a live quality score. fresh, warning, degraded. that's it. no backend, no login, nothing stored. just reads what's happening and tells you called it slate. it's free. [https://chromewebstore.google.com/detail/dgkgpdchcpofkfhcfapmlljfigchfjjk?utm\_source=item-share-cb](https://chromewebstore.google.com/detail/dgkgpdchcpofkfhcfapmlljfigchfjjk?utm_source=item-share-cb) https://preview.redd.it/nxkh6hanv32h1.png?width=1280&format=png&auto=webp&s=5a1588cb7283a8375c570a4633547b102850b5c5

I open-sourced the content SEO pipeline I run entirely in Claude Code — 15 min/day, $0.45/post, real numbers inside

https://preview.redd.it/n0eypvqm032h1.png?width=2266&format=png&auto=webp&s=e7c83a8df3127463e71d37ae22dbeda9538453d3 I've been running a content SEO/AEO operation through Claude Code for about a year and finally cleaned up the slash commands into something forkable. Sharing because the Claude Code crowd is the right audience for this pattern. The pipeline is 7 slash commands chained together. Each command is a markdown file in .claude/commands/ with a strict role + output contract — Claude reads pipeline.yaml for state, runs one step, pauses at a human gate, and updates the state file. Stateless re-entry, so I can stop mid-post and pick up next day with /seo-daily. The flow: /seo-research (Perplexity Deep Research API, \~$0.45/post) → /seo-brief → /seo-write → /seo-optimize (10-check scorecard) → /seo-publish (Sanity HTTP API → IndexNow ping). 4 human gates so I keep judgment over angle, brief, copy, and publish decision. One brand I run this for: 131 → 964 avg impressions/day in 12 months (7.3×). Monthly impressions 2,142 → 39,240 (18×). Blog content from this pipeline drove 51.8% of all GSC impressions across 119 posts. Honest caveat — clicks didn't grow proportionally because titles/meta weren't tuned for CTR yet; that's the next iteration (/seo-refresh command in roadmap). Technical things I'd flag for anyone considering similar: \- Sanity MCP's create\_documents\_from\_json overwrites your custom \_id with a UUID, breaking deterministic frontends. The publisher uses Sanity's direct HTTP mutation endpoint instead. Documented in the repo. \- Brand voice lives in one YAML (config/seo-settings.yaml). The commands read it; no hardcoded brand anywhere. Fork → swap one file → you're running your brand. \- Pluggable CMS — Sanity is the reference impl but swapping to WordPress/Contentful/Webflow is one file edit. Repo: [https://github.com/viren040/content-seo-orchestrator](https://github.com/viren040/content-seo-orchestrator) (MIT) Genuinely curious what other patterns Claude Code users are running for content/marketing ops. The slash-command-as-pipeline pattern feels under-explored.

I built a small Chrome extension for my own Claude workflow, sharing in case it helps others

Hey everyone, I’ve been using Claude a lot for writing and coding, and over time I noticed a few friction points in my workflow. It's mostly around navigation, exporting, and reusing chats across tools. So I ended up building a small Chrome extension for myself and I’ve been iterating on it recently. Right now it does a few simple things: * Adds navigation inside long Claude chats (makes it easier to jump between parts of a conversation) * One-click export of chats to `.md` * Export “plans” or structured outputs as `.md` * Quick action: copy conversation and send it to other tools (Gemini / ChatGPT / Perplexity) for second opinions It’s still very much a “built for my own workflow” kind of tool, and I’m actively tweaking it as I use Claude more. If anyone is curious, here’s the extension: [Claude Code Enhancer Chrome Extension](https://chromewebstore.google.com/detail/claude-code-enhancer/agefagkplnpjloalhpbpkfmadidjkepi) Would be interested to hear how others are handling: * exporting Claude outputs * cross-checking responses with other models * managing long conversations

Scaling LLMs horizontally: hidden-state coupling without weight modification [R]

Residual Coupling (RC) connects frozen language models in parallel using small, learned linear bridge projections. These bridges read hidden states from one model and inject additive updates into the residual stream of another at intermediate layers. In bilateral setups, simultaneous return bridges form a feedback loop that stabilizes both streams without altering base weights. This architecture establishes a two-step paradigm where base models function as memorizers, while lightweight linear bridges handle cross-domain generalization. Constraining the bridges to purely linear maps prevents overfitting because they can only map existing geometric relationships between the frozen representation spaces. As the bridges are optimized against ground-truth target data, they have no incentive to map ungrounded features such as individual models' hallucinations. Keeping the base weights completely frozen eliminates catastrophic forgetting. The system maintains operational closure, transforming inputs through its existing structure rather than changing to accommodate them. Evaluating bilateral RC against Mixture-of-Experts (MoE) routing across the same frozen models shows these results: * Medical (3-model): Reduces perplexity to 11.02, compared to 56.80 for MoE and 57.08 for the frozen baseline. This represents an 80.7% reduction. * TruthfulQA Health (MC1): Improves accuracy by 9.1 percentage points over the baseline. Independent models have uncorrelated hallucinations, allowing the bridge gates to amplify consistent cross-model updates while suppressing individual errors. * Coding Test: CodeGPT-small-py and GPT-2 use different tokenizers, causing a 7-million baseline perplexity on mismatched text. MoE reaches 878, but RC achieves 5.91 by reading hidden states before the output projection collapses. This framework introduces a horizontal scaling axis for multi-model systems, moving beyond vertical scaling via larger monolithic models. Latency remains bounded by the slowest single model. Specialists can be added or removed without retraining the remaining system. In some scenarios, this architecture could replace multi-turn text prompting in agentic workflows with a single parallel forward pass, allowing models and/or bridges to run on separate nodes or edge devices without a central bottleneck. By decoupling memorization from relational alignment, RC bridges provide a framework for scaling multi-model systems and offer a path toward native multi-modal integration. Paper: [https://ssrn.com/abstract=6746521](https://ssrn.com/abstract=6746521) Code: [https://github.com/pfekin/residual-coupling/](https://github.com/pfekin/residual-coupling/)

I converted Google’s AI search guidelines into a Claude skill goog-geo

Google recently published official guidance on how to optimize pages for AI-powered search features like AI Overviews and AI Mode - [https://developers.google.com/search/docs/fundamentals/ai-optimization-guide](https://developers.google.com/search/docs/fundamentals/ai-optimization-guide) Most of the advice floating around GEO / AI search optimization is still pretty hand-wavy, so I wanted something more concrete. So, I converted Google’s AI search guidance into an open-source Claude Code skill: [**https://github.com/vishalmdi/goog-geo**](https://github.com/vishalmdi/goog-geo) The skill audits any live URL and turns the guidance into a scored report: * Checks whether Googlebot can crawl the page * Checks indexability and snippet eligibility * Detects noindex, nosnippet, max-snippet, canonicals, robots.txt issues * Uses a live browser to inspect rendered DOM and JSON-LD schema * Reviews headings, semantic HTML, answer blocks, FAQs, tables, author/date signals * Checks whether AI crawlers like GPTBot, PerplexityBot, ClaudeBot, and Bingbot are allowed * Produces a 100-point GEO / AI search readiness score * Gives a prioritized action plan instead of vague SEO advice The main idea is simple - Google’s AI search features are not a totally separate SEO system. They still depend on crawlability, indexability, snippet eligibility, helpful content, and structured/extractable pages. So instead of guessing what “AI optimization” means, this skill audits against the actual signals Google documented. I also added a “what not to do” section because Google explicitly says some popular AI SEO advice is useless or misunderstood, like treating \`llms.txt\` as a Google AI ranking lever. Would love feedback from anyone working on SEO, content, SaaS landing pages, docs, or AI search visibility. If you find it useful, a GitHub star would help: **Repo Link:** [**https://github.com/vishalmdi/goog-geo**](https://github.com/vishalmdi/goog-geo)

How can I prevent Claude from doing this: “Hey, wait a minute! There’s something important I didn’t think about”?

As a first-time user of Claude AI, coming from Gemini, Perplexity, and Genspark, I’m really amazed by the wonderful things Claude can do. However, I’ve noticed that in almost every project or chat, it seems that Claude intentionally saves the best things to say for the end of the conversation. For example, if I ask to analyze a text or some code, or ask for suggestions on how to do things, it starts providing a lot of information and indications on what to do, and then says, “But wait! There’s this fundamental thing I didn’t think of before, this changes everything!!” What the f\*\*\*?! I was already starting to execute, or I read a wall of text and then you said the exact opposite. It’s as if the reasoning is exposed but not tagged as reasoning (Gemini tags its reasoning with a different font dimension). Also, sometimes it seems like it purposely wants to prolong the conversation. Let’s be clear, I love the final result, much better than the aforementioned LLMs, but this is something I’m still not embracing yet.

Free MCP server that audits pages for AI-citation eligibility (13 tools, no API keys)

I've been thinking about a gap in the MCP ecosystem: there are tools for web search, document reading, and code execution, but nothing that audits a page for the signals AI assistants actually use when deciding what to cite. So I built one. The AI-SEO MCP gives Claude (and any other MCP-compatible agent) 13 tools to audit, score, and rewrite pages for AI-citation eligibility. The things it checks are the ones that matter specifically for AI search - not classic SEO factors: \- FAQPage JSON-LD schema (structured answers are what AI assistants extract) \- robots.txt posture per AI crawler - GPTBot, OAI-SearchBot, PerplexityBot, ClaudeBot, and 7 more \- llms.txt presence and spec compliance \- Citation worthiness score broken down by engine (Perplexity, ChatGPT, Google AI Overviews, Claude) \- Entity density and sameAs link coverage \- Two rewrite tools (rewrite\_for\_aeo and rewrite\_for\_geo) that use MCP sampling to have Claude actually do the rewrite under a structured rubric Install is one npx line: \`\`\` npx -y u/automatelab/ai-seo-mcp \`\`\` Then add the usual config block to claude\_desktop\_config.json. No API keys. No registration. MIT license. It fetches public URLs directly and respects robots.txt by default. One thing I found useful while building it: GPTBot and OAI-SearchBot are separately controllable in robots.txt, but most sites either block both or allow both. The MCP surfaces this - you can block GPTBot (training) while explicitly allowing OAI-SearchBot (ChatGPT search retrieval). That distinction alone has been worth adding to the audit for a few sites I've tested it on. Happy to answer questions about the implementation or what the audit output looks like in practice. Repo: [https://github.com/AutomateLab-tech/ai-seo](https://github.com/AutomateLab-tech/ai-seo) Landing: [https://automatelab.tech/products/mcp/ai-seo/](https://automatelab.tech/products/mcp/ai-seo/)

Backlash against Arxiv's proposed 1 year ban is genuinely perplexing. [D]

Anyone else surprised at the enormous amount of backlash against Arxiv's proposed 1 year ban for authors and coauthors publishing papers with hallucinated reference and other obvious LLM/Gen AI artifacts? [https://x.com/tdietterich/status/2055000956144935055](https://x.com/tdietterich/status/2055000956144935055) [https://xcancel.com/tdietterich/status/2055000956144935055](https://xcancel.com/tdietterich/status/2055000956144935055) Some of the responses: 1. "This is the age of AI, Arxiv should be part of the movement instead of holding onto the old ways" 2. "The P.I. is a macro-manager, not a micro-manager, can't be expected to read every reference that his/her student puts in." 3. "I publish 20+ papers a year with my students, how do you expect me to read everything?" 4. "What about teams with 100s of people? How can you expect the authors to check references?" 5. "Who reads references in depth anyways!?" These responses are very revealing how academia works. Apparently people have just been slapping names on research papers they've never even read or fact-checked themselves. Very obscene!