Perplexity

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Perplexity AI

Learn more about Computer for Counsel: https://t.co/aX4TiR72a7

Learn more about Computer for Counsel: https://t.co/aX4TiR72a7

Introducing Computer for Counsel. Computer now connects the research databases, document tools, and matter-management systems lawyers use every day. Pull citable sources from @midpageAI, @LegalZoom,

Introducing Computer for Counsel. Computer now connects the research databases, document tools, and matter-management systems lawyers use every day. Pull citable sources from @midpageAI, @LegalZoom, @Docusign, @netdocuments, and more. Available for all Pro and Max subscribers. https://t.co/El3028Ua7P

I scanned 50 SaaS websites for AI readiness. Most failed the same 3 things

I’ve been digging into “AI visibility” lately — basically whether tools like ChatGPT, Claude, Perplexity, etc. can actually understand and recommend a SaaS company. Not SEO in the classic sense. More like: if someone asks an AI tool “what’s the best software for [use case]?”, does your site give the AI enough clear information to confidently include you? I ran 50 SaaS sites through an AI-readiness scanner and kept seeing the same issues. Crawler access was messy Some sites were blocking or limiting AI crawlers without realizing the downstream impact. The site works fine for humans, Google can often still index it, but AI systems may not be able to access or interpret key pages properly. The homepage copy was way too vague Lots of “streamline workflows,” “empower teams,” “scale faster,” “AI-powered platform” type copy. That might pass the vibe check for humans, but it’s not great for machines. AI systems need clear context: What category are you in? Who exactly is it for? What job does it do? What tools do you replace? What are the main use cases? What makes you different? If that isn’t obvious, the AI will either summarize you badly or ignore you. Weak structured data / missing machine-readable context A lot of sites had missing schema, vague pricing pages, thin docs, unclear product pages, no comparison pages, or no simple summary of what the company actually does. I find that most SaaS websites are optimized for human visitors, but not for AI agents. That’s probably going to matter more as buyers start using AI tools for product discovery and comparison. I used a tool to run the checks, DM if you want it. Not sue if I will get banned if I add it :) It gives a quick score and shows issues around AI crawlability, schema, pricing clarity, sitemaps, and whether AI tools can understand/recommend the business. Feels like “AI readiness” might become a technical SEO checklist item pretty soon. Is anyone here actively working on this yet, or are you waiting until there’s clearer evidence it drives pipeline? submitted by /u/Emma-Lawrencee [link] [comments]

AI Moderator

​ I'm getting increasingly frustrated trying to get research done because of how restrictive Claude, Perplexity, ChatGPT, etc. have become. ​ It feels like it's getting worse over time. The moderation is excessive, and there's no human common-sense check before my questions gets shut down. ​ Does anyone have recommendations for a platform that's less restrictive — or has no moderation at all? ​ I can't even ask a simple question without getting stonewalled. ​ submitted by /u/ChurchOVSatan [link] [comments]

Every memory links back to the session, file, or source it came from with full transparency and control. You can access Brain and your saved memories under "Customize" in the sidebar. Read more abou

Every memory links back to the session, file, or source it came from with full transparency and control. You can access Brain and your saved memories under "Customize" in the sidebar. Read more about Brain: https://t.co/lExdtp176d

With Brain, Computer starts each task with full context of your projects, decisions, and sources instead of from scratch. On tasks that require past context, Brain improves answer correctness by 25%,

With Brain, Computer starts each task with full context of your projects, decisions, and sources instead of from scratch. On tasks that require past context, Brain improves answer correctness by 25%, recall by 16%, and runs 13% cheaper per task. https://t.co/xlNHTmA1My

Introducing Brain in Computer. Brain is a continuously learning memory system. Every task on Computer plugs into a context graph built by Brain. It makes Computer more stateful with every run. Avai

Introducing Brain in Computer. Brain is a continuously learning memory system. Every task on Computer plugs into a context graph built by Brain. It makes Computer more stateful with every run. Available as a research preview for all Perplexity Max subscribers. https://t.co/Dw4Q7Izmqs

OpenAI's market share falls below 50%

submitted by /u/Far-Commission2772 [link] [comments]

Most of this "AI marketing" drama is just prompting with better packaging. And it's a shame.

Look, I get it. Marketing is exhausting. Ten hours building a feature feels productive. Ten hours "marketing" it feels like screaming into a void. That frustration is real and valid. But here's the thing — a lot of these tools being sold to you right now are not solving that problem. They're just monetizing your confusion about it. "Understands your brand" = you gave it a paragraph about your product. "Writes like you" = you fed it a few examples. "Finds relevant users" = keyword search on Reddit and Hacker News. "Proven viral templates" = someone copied top posts and labeled them viral. "Strategy buddy" = a follow-up prompt that says "how's my growth doing?" That's it. That's the product. Dressed up in a landing page. What's actually going on under the hood Two concepts do most of the heavy lifting in these tools, and you can build both yourself in under an hour: PRD (Product Requirements Document): This is just a document that explains what your product is, who it's for, what problem it solves, and what makes it different. It's the map. You write it once, you hand it to any AI model, and suddenly the AI has actual context instead of guessing. No app needed. A Google doc works fine. Governance file: This is just a ruleset you give the model. Your tone, your audience, what you will and won't say, what sounds like you and what doesn't. Think of it as a brand bible in plain text. Every good AI workflow has one. Most paid tools are just hiding theirs from you so you feel dependent on them. Combine those two with a halfway decent prompt inside ChatGPT, Claude, Gemini, or Perplexity — tools you probably already have — and you have 90% of what's being sold here. For free. Right now. Today. The DIY walkthrough If you want to do this yourself, here's the actual workflow: Write a one-page PRD. What is the product, who needs it, why does it matter, what makes it different. Write a governance file. Your tone, your audience, things you will and won't claim, examples of good responses. Build a small prompt library. One for post drafts. One for replies. One for researching where your audience actually hangs out. Review everything manually before posting. Automation without judgment is just spam at scale. Track what actually gets replies, clicks, and signups. Not impressions. Real signals. Do a quick audience survey. Ask your actual users what they care about. That's more useful than any "strategy buddy." That's it. No subscription. No dashboard. Just structure and iteration. On vibe coding and vibe marketing Vibe coding lowered the floor for builders, which is great. But it also lowered the floor for people packaging half-finished ideas as products and selling them before anyone's verified they work. A few hours of real prompting beats a month of automated noise. When your output is generic, people notice. You're not just wasting time — you're actively damaging your own brand. Every spammy reply, every recycled template, every GPT-flavored post is a withdrawal from the trust account you're trying to build. The real bottleneck in marketing has never been generating text. It's knowing who actually gives a damn, where they are, and what to say to them specifically. No wrapper app solves that. You still have to think. If you want to actually learn this stuff Don't buy a tool. Read a few posts from real builders first. Pick a newsletter from an actual developer — not a "growth hacker," not a LinkedIn influencer, someone who ships things and writes about what worked and what didn't. Spend fifteen minutes on the porcelain throne reading how someone structures their workflow. Not to copy it. Just to understand the steps, read the critique, and figure out what you'd do differently. Then make your own version. Test it. See what lands. That's how you build something with actual signal behind it. The builders I respect most put their tools on GitHub with a readme and say "if this helps you, great — and if it teaches you to make your own, even better." That's the energy. That's how you stay on the right side of this. If you have a tool that genuinely helps — say so. Drop it in the comments with what it actually does and what it doesn't do. Honest is better than hyped. If you have a shorter version of this, a better explanation, or a workflow that worked for you — please add it. The goal here isn't to be right, it's to make sure people have what they need to make an informed decision. TL;DR Most "AI marketing" tools are a PRD and a governance file in a trench coat. You can build both yourself in an hour with any AI model you already have. Learn the workflow. Read the critique. Make your own version. Ten followers and a polished pitch is theater, not strategy. If you learned nothing else, go read one real builder's workflow before you buy anything. submitted by /u/Mstep85 [link] [comments]

The biggest bottleneck in my AI workflows turned out to be me

After months of using GPTs for development, research, planning, debugging, and business work, I noticed something strange. ​ The model usually wasn't stuck. ​ I was. ​ The workflow kept pausing because the system needed another prompt, another confirmation, another "continue." ​ So I started experimenting with a different question: ​ What happens if AI conversations can keep progressing without constant human intervention? ​ That became Ghost in the Loop. ​ An open-source browser tool that automatically continues multi-step conversations across ChatGPT, Claude, Gemini, Perplexity, DeepSeek, Copilot, Grok, Manus and other AI platforms. ​ Some things it's helped with: ​ • Long-form research • Multi-step coding tasks • Roadmap execution • Prompt queues • Iterative refinement loops ​ Now I'm trying to figure out where the approach falls apart. ​ What concerns would you have with a tool like this? ​ What failure modes would worry you? ​ What would make something like this useful rather than dangerous? ​ GitHub: https://github.com/MShneur/ghost-in-the-loop ​ TL;DR ​ Built an open-source AI workflow automation tool. ​ Trying to learn where autonomous AI workflows become genuinely useful versus where they become a bad idea. submitted by /u/Mstep85 [link] [comments]

I gave your agent access to Firefox - meet Firefox CLI

Firefox CLI is a CLI interface that lets your agent control your real Firefox session. It's a full equivalent of Agent Browser with the same capabilities, but for Firefox - and with a number of improvements. Why it's better First, you install the extension once and for all. The extension ships right alongside the CLI: install it, grant access, forget about it. Unlike Chrome, where you have to grant connection permissions every half hour and manage debugging sessions - here it's one button and full control. Second, your agents can now create their own separate windows and request your permission to connect on their own. In everything else, Firefox CLI mirrors Agent Browser: token-efficient operation via short IDs, running arbitrary scripts, keypresses, input emulation, form filling, and full tab and window management of your real session - where you're already logged in. Why I built it I used the Comet browser for a long time (on my promo subscription to Perplexity), but it started to let me down. More unnecessary features and ads crept in, it got slower. But the main thing - using Comet as an actual browser during development is extremely inconvenient: there's music you can't turn off, a broken onboarding that was never fixed after months of back-and-forth with support, and a poorly functioning CDP. I switched back to Firefox as my main browser, but losing the ability for agents to control my browser was a huge blow to my workflow. No automation for filling out boring freelance forms, no proper web app testing. I went looking for alternatives, but nothing like Agent Browser for Firefox simply existed. And here's the result :) Installation 1. Install the CLI: bash npm install -g firefox-cli 2. Install the Firefox extension: bash firefox-cli setup 3. Install the skill for agents: Claude Code text /plugin marketplace add respawn-llc/claude-plugin-marketplace /plugin install firefox-cli@respawn-tools Codex text $skill-installer install https://github.com/respawn-llc/firefox-cli/tree/main/skills/firefox-cli General bash npx skills@latest add respawn-llc/firefox-cli The project was built by Builder autonomously over 62 hours of continuous work. submitted by /u/Nek_12 [link] [comments]

Learn more about Deep Research in Computer: https://t.co/YhFZVvTb3j

Learn more about Deep Research in Computer: https://t.co/YhFZVvTb3j

Deep Research in Computer is built on our Search as Code architecture. The model writes code that assembles search itself, running thousands of retrieval steps in parallel, tailored to each question.

Deep Research in Computer is built on our Search as Code architecture. The model writes code that assembles search itself, running thousands of retrieval steps in parallel, tailored to each question. It outperforms legacy Deep Research on every benchmark. https://t.co/0BrQjt8iGc

We're integrating Deep Research as a native skill inside Computer. It now connects to the agent harness that powers Computer, with access to search as code generation, long running sandboxes, connect

We're integrating Deep Research as a native skill inside Computer. It now connects to the agent harness that powers Computer, with access to search as code generation, long running sandboxes, connectors, tools, and licensed data. Available now to Pro and Max subscribers. https://t.co/uHpVISkh2P

A loss less handoff + wiki skill to manage long running work across multiple claude sessions and multiple LLMs seamlessly

Sharing a skill I use to manage long-running work across sessions and across multiple LLMs: One SKILL.md file, no dependencies. At the end of every session it writes a handoff (exact numbers, decisions with rationale, open questions, next action) and appends the durable facts into a small project wiki that contains - a PROJECT.md which serves as wiki home page with mission, requirements, key numbers, and a decisions log that need to survive multiple sessions - Topic pages for any additional data and information generated during the session (like a research or plan created in that session) - An index.md file that maps to all the topic files. and handoff file First run of the skill creates the wiki and every later run appends more context files to it. Click here for the skill Benefits: Token cost stays low. A new session reads PROJECT.md (capped at ~150 lines) and the latest handoff, instantly knows what the project is about and what to do next. No re-exploring the repo, no replaying old conversations. Everything historical is indexed with keywords, so it's one grep away but never loaded by default. Context cost is flat whether the project is two days or two weeks long. Nothing is lost to compaction. When context is about to compact — or the session just ends, the state is already on disk. The next session picks up the exact information, requirements and decisions. Switching LLMs is instant. This is the part I didn't plan but now use most. The system is plain markdown files, so it isn't locked to Claude. When my Claude usage hits its limit mid-task, I point Codex or Perplexity at the same PROJECT.md and latest handoff and keep working — same facts, same decisions, same next action. When Claude resets, I come back the same way. The wiki doesn't care which model reads it. Hope this helps. submitted by /u/coolreddy [link] [comments]