Perplexity

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

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. submitted by /u/r0sly_yummigo [link] [comments]

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://preview.redd.it/nxkh6hanv32h1.png?width=1280&format=png&auto=webp&s=5a1588cb7283a8375c570a4633547b102850b5c5 submitted by /u/-HydrogeN [link] [comments]

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 (MIT) Genuinely curious what other patterns Claude Code users are running for content/marketing ops. The slash-command-as-pipeline pattern feels under-explored. submitted by /u/HeatPrevious1395 [link] [comments]

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 Would be interested to hear how others are handling: exporting Claude outputs cross-checking responses with other models managing long conversations submitted by /u/PlentyButterfly4462 [link] [comments]

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 Code: https://github.com/pfekin/residual-coupling/ submitted by /u/kertara [link] [comments]

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 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 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 submitted by /u/vishal_jaiswal [link] [comments]

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. submitted by /u/FinnedSgang [link] [comments]

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 Landing: https://automatelab.tech/products/mcp/ai-seo/ submitted by /u/exto13 [link] [comments]

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://xcancel.com/tdietterich/status/2055000956144935055 Some of the responses: "This is the age of AI, Arxiv should be part of the movement instead of holding onto the old ways" "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." "I publish 20+ papers a year with my students, how do you expect me to read everything?" "What about teams with 100s of people? How can you expect the authors to check references?" "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! submitted by /u/NeighborhoodFatCat [link] [comments]

When ChatGPT cites your website, how often does anyone actually click through?

Curiosity-driven question. I've been tracking AI referral traffic via Zen Reports across a handful of sites, and ChatGPT's click-through rate to cited sources seems much lower than Perplexity's. Perplexity has a more prominent citation UI and seems to drive more direct traffic. Happy to share more about my setup if it's helpful ; always curious how others are approaching the same problem. There's clearly no industry-standard answer yet, which is why I'm asking here. ChatGPT citations seem to drive traffic primarily when the user goes to do further research. Anyone have data or intuitions on how different AI interfaces affect citation click-through behavior? submitted by /u/Tasty-Win219 [link] [comments]

Build dashboards and automations from your Snowflake data for pipeline analysis, product usage, customer segments, and more. Admins maintain control over access, business definitions, and shared data

Build dashboards and automations from your Snowflake data for pipeline analysis, product usage, customer segments, and more. Admins maintain control over access, business definitions, and shared data logic across the organization. Learn more: https://t.co/ysqRRycbyJ

Computer now connects to Snowflake. Run end-to-end work against live warehouse data and get answers with SQL, source tables, filters, and metrics. It’s like a personal data science team, on call wit

Computer now connects to Snowflake. Run end-to-end work against live warehouse data and get answers with SQL, source tables, filters, and metrics. It’s like a personal data science team, on call with accurate answers from live company data. https://t.co/L1uQC6u5zZ

External content is scanned in parallel by ML classifiers and the BrowseSafe model before agents act on it. File connector data is encrypted in transit and at rest, uploaded files automatically delet

External content is scanned in parallel by ML classifiers and the BrowseSafe model before agents act on it. File connector data is encrypted in transit and at rest, uploaded files automatically delete after 7 days, and more. Read more on the blog: https://t.co/gAomEQkvxi

Computer is secure by default. Every task runs in its own hardware-isolated sandbox with VPC-level storage and compute separation. Agents are authenticated with short-lived proxy tokens instead of ra

Computer is secure by default. Every task runs in its own hardware-isolated sandbox with VPC-level storage and compute separation. Agents are authenticated with short-lived proxy tokens instead of raw API keys. https://t.co/ohIjY3dboB