Apify

Apify AI

Apify AI

Apify AI

Apify AI

Apify AI

Apify AI

Apify AI

Apify AI

Apify AI

Apify AI

Claude hallucinated its own internal tools, freaked out, and accused me of a prompt injection attack 💀

Ran into a fascinating UI/pipeline bug today while pasting standard text from a job board into Claude. As you can see in the screenshot, the backend text compaction or tool-calling layer leaked its own JSON definitions (referencing Apify/Notion tools) directly into the processing context. Because the security guardrails detected raw system tags where they shouldn't be, the model threw a false-positive prompt injection warning, blaming the input text. Curious if anyone on the engineering side has insights into how Anthropic structures these background tool injections and why the sanitation layer occasionally drops them into the user-facing chat. submitted by /u/Enough-Piano-2362 [link] [comments]

I built an opensource Claude Code skill for Fiverr optimization since there wasn't any

Most "AI Fiverr tools" quietly tell the model to mentally estimate how many competitors a keyword has. That number is hallucinated — it changes every run. I wanted the opposite, so I built fiverr-gig-optimizer as a proper Claude Code skill (SKILL.md format, installable as a plugin). The core design rule: every market number — competition counts, demand signal, competitor pricing — comes from a deterministic Python script working on real data. The LLM layer never produces one. If the data isn't there, the skill asks you or says it doesn't have it. The model only writes copy (titles, descriptions) and your own offer choices (delivery times, package contents). How it works technically: query_dataset.py — keyword lookup against the bundled sample dataset, returns a gig_count + match_confidence (HIGH/MEDIUM/LOW). Low confidence → the skill asks you to paste the Fiverr count instead of guessing. score_keyword.py — piecewise-linear competition score over log10(gig_count), anchored so tier labels map intuitively. All constants in a tunable JSON config. analyze_pricing.py — per-tier percentiles (p25/median/p75) of real competitor prices. Too few samples → flagged low-confidence, not fabricated. A vendored Perseus/__NEXT_DATA__ reader for live scraping that uniquely recovers the real "X services available" search total — something no off-the-shelf Apify actor returns. There's also an opt-in community dataset on Hugging Face. Scrape your niche, strip PII, contribute back — the free bundled data gets better over time. Install: /plugin marketplace add Ahad690/fiverr-gig-optimizer /plugin install fiverr-gig-optimizer@fiverr-tools GitHub: https://github.com/Ahad690/fiverr-gig-optimizer Dataset: https://huggingface.co/datasets/Ahad690/fiverr-gigs Would love feedback on the SKILL.md structure and the scoring formula — both are visible and tunable. Happy to answer questions about the Perseus reader approach for gig_count_in_search if anyone's interested. submitted by /u/Nocare420 [link] [comments]

I stopped opening Claude Code to "check things." Now it runs scheduled agents and pushes me a brief every morning. Here's the whole setup.

For months my Claude Code usage was reactive. I'd open it, ask it to scrape something, read the output, close it. Every morning I was manually kicking off the same five things: job listings, what creators I follow shipped, crypto prices, trending repos. That's not leverage. That's a chatbot with extra steps. So I flipped it. Claude Code now runs on a schedule and I just read the results. Here's the pattern, because almost nobody talks about Claude Code as a cron runner and it's the highest-leverage thing I've done with it. The core idea A "routine" is a scheduled agent run. Cron fires, Claude wakes up, does one job, writes the output to a file, and (optionally) pings me on Telegram. No UI babysitting. The agent isn't live-chatting with me, it's doing a defined task and leaving a paper trail. My current routines: morning-brief (7am) reads everything the overnight scrapers dropped and writes one digest creator-radar pulls the latest videos from the 3 creators I actually learn from, so I'm not doomscrolling YouTube remote-jobs-scraper (Apify) dumps fresh roles into an inbox folder, filtered to what I'd actually apply to crypto-pulse and github-trending for market and tooling signal Each one is dumb on its own. Together they replaced about 40 minutes of morning tab-opening. The structure that made it work Three things, kept separate: A scheduler that owns cron and nothing else. It decides what runs when. It does not contain task logic. One file per routine. Each routine is a self-contained script with one responsibility. morning-brief.mjs does not know how creator-radar works. This is the part people get wrong: they build one giant agent that does everything, it breaks, and they can't tell which part failed. A state file. Last-run timestamps, what's already been seen, what's new. Without this your "what shipped today" routine re-reports the same thing every run and you stop trusting it. The output of every routine is a file with frontmatter (type, source, date, status: new). That frontmatter is what makes the morning brief possible: it just globs everything marked new, summarizes, marks them read. The routines don't talk to each other. They talk to the filesystem. The gotchas that cost me time Don't let agents run live and outbound on day one. Mine draft and read-only first. The rule I wrote down: apprentice, not autopilot. An agent that can send emails on a cron at 3am while you sleep is a bad first version. Idempotency or it's noise. If a routine can't tell what it already processed, every run is a duplicate. State file first, logic second. One job per routine. When something breaks at 7am you want to know it was the jobs scraper, not "the morning thing." Pin the output format. I have the agent write structured frontmatter every time. Free-form output means the downstream brief can't parse it and you're back to reading raw dumps. Separate worlds with hard walls. I run a tech world and a totally separate personal one. The scheduler never lets one read the other's files. If you mix contexts your agent starts "helpfully" blending things you wanted apart. Why this beats prompting on demand The value isn't any single routine. It's that the cost of "I wish I tracked X" drops to writing one more small script and adding a cron line. Once the scheduler + state + one-file-per-job skeleton exists, every new automation is 20 minutes. They compound. After a month I had a personal ops layer that I never sit and operate, I just read it over coffee. If you're using Claude Code only when you remember to open it, you're leaving the best part on the table. Give it a schedule and a filesystem and let it work while you don't. Happy to share the scheduler skeleton if people want it. submitted by /u/Remarkable-Ad8580 [link] [comments]

Built a little widget showing current session usage as well as weekly usage.

I know there are a lot of these already as official apps but they include a lot of what I don't actually need. I kept on having a browser tab open and clicking on claude.ai session then usage to see where I was at and assumed there must be a better way. So like we all do, asked Claude to build me something. Had a lot of back and forths with it not keeping sync or restarting due to different things, but this version (although not to the second - I only poll every 10mins but fine for me) seems to be working so far. How it works (and i did get claude to help to explain this) is: - A small script runs every 10 minutes in the background. - It grabs your existing Claude Code login token (the one Claude Code already saved in your Mac keychain) and makes one tiny API call to Anthropic. - That call sends a single word and asks for 1 token back. Its only purpose is to read the "usage limits" info that Anthropic attaches to every reply. The reply is basically free and bills to your Claude subscription, not pay-as-you-go credits. - It saves those numbers to a file on your Mac (~/.claude/session-usage.json). The widget just reads that file and draws the card. You can find it here hope some of you find it helpful: https://github.com/leechild4/claude-session-widget I found it also useful to pair with my https://github.com/leechild4/sleepwork-plugin as knowing when a session might finish after I've headed off to bed this one helps you schedule any job for a future time or date (this was helpful when my free apify tokens ran out and i scheduled it to finish the job then they renewed in a few days. You just type /sleepwork scrape all the transcripts from the 8 most recent videos from Nate Herk at 2am . Then at 02:00 your Mac wakes (if asleep), turns your request into a proper brief, runs the full Claude Code agent headless against it, tests its own work, writes a results file, and deletes its own scheduled job. Now I admit the below is what claude wrote but its what we chatted about as to why all the other existing features didn't do exactly what I wanted: - /schedule (cloud Routines) — runs on Anthropic's servers against a fresh clone. Wrong machine: it can't touch my local files or start my local servers. - Desktop scheduled tasks — runs locally (closest option), but the Desktop app has to stay open and awake the whole time. That open-app requirement is the friction I wanted gone. - /loop — runs locally, but the terminal/session has to stay open. Close it and the job dies. - CronCreate / ScheduleWakeup — same limitation, they're session-scoped helpers, not standalone. - GitHub Actions / Agent SDK — workable, but it's more setup and it's repo-driven, not "my whole machine overnight". sleepwork instead wraps the headless CLI (claude -p) and schedules it at the OS level with launchd (macOS's built-in scheduler), so nothing has to stay open. The Mac can be asleep — launchd fires the job on next Anyway hope some of you might enjoy it and find it useful. submitted by /u/leechild4 [link] [comments]

Apify not installing

My Apify is not being installed. It’s step 1 of the process. I have created an account on apify website. I’m not the most technologically advanced so I have no clue why it's not being downloaded. If you have simple steps for me or can recommend a video to watch for dummies, I would really appreciate it! Thank you submitted by /u/azulalee207 [link] [comments]

7 ways I actually wire AI into my marketing work (the boring practical stuff, not the hype)

None of it is magic. It's just removing the parts of the job that don't need a human. I'm Curious what everyone else is wiring in. 1. I run all terminal installs inside a virtual machine https://preview.redd.it/3287jk1gyo4h1.png?width=2024&format=png&auto=webp&s=488efdc39223d26307e57d837888233fa351b8e4 Running code on a company laptop is a security minefield. First time a dev caught me pulling packages with npx, alarms went off (fair enough). Now anything that installs or downloads through the terminal runs inside a UTM, fully isolated from the real system. If it breaks, it breaks in a sandbox. 2. I pull audience data off almost any platform with Apify Reddit, LinkedIn, X, anything else. There's usually an Apify actor for it. I find the right one and have Claude Code write a small Python script that pulls exactly the audience data I'm after. 3. I buy SERP data on demand instead of subscribing I got tired of another monthly bill, so I switched to topping up token packs and pulling SERP data only when I need it. A daily make scenario saves the results to storage, so the tracking basically runs itself. 4. I scrape competitors' GitHub Issues to figure out what to build https://preview.redd.it/oajhtithyo4h1.png?width=2784&format=png&auto=webp&s=5c991fbe62f7ea30a59f1ba480674bd7d1e8d27c To work out what a software dev should add to a tool we are promoting, I scrape the Issues tab of competitors' repos. GitHub token + a Python script in Claude Code. That's how I found a massive backlog of unaddressed complaints about our biggest rival. 5. I run SEO tasks through open-source Claude Code skills There's a free open skill set that plugs into Claude Code for SEO work. I extended mine with a Google Keyword Planner API connection so I get search-volume data for free. The repo's open, you just wire in your own SERP API key. 6. I generate captions and filler shots in code with Remotion Right at the end of a video edit, Remotion + Claude Code handle the finishing touches. A few animated filler shots and burnt-in captions, all defined in code. Remotion's free for commercial use within certain limits, which is nice. 7. I hand-finish animations in Rive or Jitter https://preview.redd.it/cbglzuqiyo4h1.png?width=2230&format=png&auto=webp&s=fdd82f382eaf92f95e35eef651fc428972a3a64b Claude Code gets me most of the way, but its animations look... not good. So I finish by hand in Rive or Jitter. You can craft genuinely polished motion in a few minutes there. AI for the grunt work, human eye for the polish. submitted by /u/Independent-Elk-1019 [link] [comments]

I had Claude Opus 4.8 build me a custom 'operating system' for my business while I was at the vet

I've been trying to cut down the number of tabs I open every morning to run my content business. YouTube analytics in one place, competitor channels in another, a notes doc for trending stuff, skills I keep re-running by hand. So I tried something. I opened a blank folder, gave Claude a rough plan, and told it to build me a single dashboard that pulls all of it into one place. First I used plan mode to map it out. It asked me a bunch of clarifying questions (what to track, web dashboard vs morning briefing, which APIs I had). Then I dropped in my design system files so it would match my brand. Then I switched to Opus 4.8, turned on the new Ultra Code mode, and told it to execute the plan. Then I left to take my dog to the vet. Came back and it had built the whole thing. One panel for trends and drops in my space, one for competitor videos and their top comments, one for my YouTube stats, one for active projects, and a launchpad to run my most-used skills. The part that actually surprised me is how Ultra Code works. There is an orchestrator that spawns sub-agents to do the work, and then a second layer of sub-agents whose only job is to check the first layer's work. That verification layer is why it can run that long without me sitting there approving everything. First pass was not perfect. Everything had the same visual weight and the skills opened a separate terminal window. One more round of feedback (bento layout, embedded terminal, Apify for the LinkedIn and IG data it could not reach) and it was genuinely usable. Honest caveat: this is the most expensive way to run Claude right now. Ultra Code plus Opus 4.8 burns a lot of tokens. For a one-off deep build it felt worth it, but I would not leave it running on autopilot for small stuff. Anyone else messing with the multi-agent verification setup yet? Curious if the self-checking layer holds up on bigger codebases. submitted by /u/Drogoff1489 [link] [comments]

Claude as my Aggressive CEO :)

Yesterday Claude killed me. I'm building something that uses Apify. I tried to connect to an actor and find out it needs the monthly subscription. I'm on pay-as-you-go right now, so I start looking for alternatives that don't require a sub. Couldn't really find anything good. Then Claude comes back with this: "You've burned an hour of your time and half an hour of mine on this. At this point it already costs more than the subscription, and you'll hit that usage level anyway. Go get the subscription and let's move on." Mild physical pressure was applied. So I bought the subscription. Claude's definitely getting a commission from them. Going back over the conversation now and it's just great. Sometimes it saves us from ourselves. submitted by /u/amitraz [link] [comments]

Any Way To Use Claude Desktop on Linux?

Hey everyone. I am new to Claude and I want to try Apify. However it requires to install desktop app but guess what? I use Ubuntu. Claude is a big and popular project and I can't believe they don't have Linux support. Is there any way to use it in Linux? submitted by /u/iv_damke [link] [comments]

Built a morning brief agent on Apify that pulls from Slack, Gmail, Calendar, and Notion

I kept switching between apps every morning trying to figure out what actually needed my attention. Slack, Gmail, Google Calendar, Notion, two or three accounts each. I just wanted something that would tell me what matters. So I built an actor on Apify to see if it could work. It fetches everything, sends the raw data to Claude via the Anthropic API, and posts the brief to Slack. Took a few iterations to get the prompt right but it genuinely gets the job done now. What I liked: it fits within Apify's $5 free plan limit, so zero ongoing cost to run it once a day. Two ways to use it: The simpler one: the actor fetches all data, calls the Anthropic API directly to generate the brief, and posts it to your Slack channel. Everything in one run. But you need to have Anthropic API key. The more flexible one: connect Claude to Apify via MCP or the API, schedule the actor to run every morning to prefetch and store the data, then have Claude read it and generate the brief on demand. Useful if you want to ask follow-up questions or regenerate without waiting for another full crawl. Live on the Apify Store if anyone wants to try it. Glad for any feedback. submitted by /u/AmbiguousSun [link] [comments]

I got sick of rebuilding the same ad research pipeline for every new client so I built something that just handles it

I got sick of reconfiguring a new stack of tools every time I took on a new app client. The workflow was always the same. Open Ad Library, find what's running, screenshot the good stuff, set up Apify, connect Airtable, wire up the pipeline, brief an editor, wait a week. Then do it all again for the next client. Tried building my own pipeline. Claude Code, Apify, Airtable, Whisper, n8n. Spent more time maintaining the infrastructure than actually running ads. So I built Zura instead. Paste any Meta Ad Library URL. It analyzes the winning creative and generates launch-ready video variations. No pipeline. No setup per client. No tooling to maintain. The time between "found a winner" and "launched a test" went from days to minutes. zura.today submitted by /u/Natural-Ad7262 [link] [comments]

Claude for Cybersecurity tasks

Just some ways in which I use Claude for cybersecurity work. Prioritizing Vulnerabilities: By uploading scan reports, asset lists, and using the Model Context Protocol (MCP), Claude can analyze and prioritize critical findings, determine patching sequences, and suggest timelines. Building Proposals: Input instructions, company collateral, and RFPs within a Claude Project to generate high-quality, succinct project proposals in minutes. Lately, I've begun using Claude Design to prepare the actual decks. Summarizing News: Claude Cowork to run a daily task that summarizes cybersecurity news of the day. Creating Threat Intel Reports: When provided with details on a breach or threat, Claude can produce professional, validated threat intelligence reports that include Indicators of Compromise (IOCs), attack chains mapped to the MITRE framework, and detection logic. Bulk Document Review: Claude Code in a specific folder, to process large quantities of documents, such as résumés for an open position. Developing Security Toolkits: Claude Code can act as a partner in building comprehensive security and compliance toolkits for environments like AWS and Azure, while helping document engineering principles and best practices. Scraping for Content Ideas: Using the Apify MCP, Claude can scrape platforms like YouTube, Reddit, and Instagram to identify trending topics in cybersecurity and AI. Automated Penetration Testing: Using open-source pentesting repositories, Claude Skills can perform thorough, authorized penetration tests against specific systems and generate comprehensive reports in a short amount of time. Acting as a Learning Guide: Claude can create personalized study programs and roadmaps, recommending relevant white papers, researchers, and practical projects based on the user's available time to help them master new topics. Filling Security Questionnaires: Claude can assist in completing long, tedious customer security checklists by leveraging previous scan results, risk registers, and security policies to provide accurate, evidence-based answers. A video where I demo these use cases is in the first comment. What are some ways in which you use Claude - or other AI tools - in your day-to-day cybersecurity work? submitted by /u/AnswerPositive6598 [link] [comments]

I built cold sales pipeline for Claude Code that launches a full campaign from one prompt

Been using Claude Code for B2B cold outreach at our agency (20+ people on the team, all of them use this daily now). We built a set of skills to solve a pretty annoying problem: our reps juggle anywhere from 5 to 10 tools per campaign. Prospecting in one tab, enrichment in another, scraping in a third, sequences somewhere else. Every campaign starts with a couple hours of this manual assembly before a single email goes out. So basically what happens: you run one command with your company website + short description of who you're going after, and Claude handles the rest. like /launch gtm-mcp.com fintech startups 10mln+ MRR in the US Builds Apollo filters, runs the search, scrapes each company's site, decides whether they actually fit your ICP (with reasoning you can read - this part alone throws out like 60-70% of Apollo results that look right in the list but obviously don't match once you actually look at the website), finds decision makers, writes the sequence, pushes it all into SmartLead in draft. You check it at two points, approve, done. it's 13 skills plus API around Apollo, Apify and SmartLead. Everything runs locally on your machine, you plug in your own API keys, nothing touches our servers, there aren't any servers on our side to touch. I'll be honest about what it doesn't do because I'm tired of the "AI 10x'd my outreach" posts: it won't fix your reply rates by itself. You still need to know who you're targeting and you still need to look at the sequences and fix whatever sounds off. The actual win is that the 5-10 tool juggling act per campaign just disappears. Same output, less time spent being a human copy-paste machine between tabs. Would really appreciate any honest takes, even brutal ones. this is completely open source and free, no hidden paywalls GitHub: https://github.com/impecablemee/gtm-mcp Instruction how to use: gtm-mcp.com submitted by /u/decaster3 [link] [comments]

I built a free claude blog skill that actually studies your business, researches competitors & keywords to find winning blog topics and high-quality articles with infographics, internal linking, product promotion, and more...

Most AI writing tools are a fancy wrapper around "give me 1500 words about X." They don't know your business, your competitors, what's already ranking, or why someone would read your article over the 10 that already exist. The output is always that same slightly-hollow, over-structured content that reads like it was written by someone who's never actually done the thing they're writing about. I wanted to build something that approached content strategy the way a good SEO consultant would like studying the business first, doing real research, then writing So I built a set of Claude Code slash commands that run a full pipeline. Here's what it actually does: Step 1: Onboarding Scrapes your website, extracts a structured business profile (product type, ICP, differentiator, brand voice, integrations), then hits DataForSEO's SERP API to find your 3 direct competitors. Everything gets saved locally in .claude/blog-config.json. You run this once. Step 2: Site Intelligence (the interesting one) This is where it gets serious. It runs three keyword sources in parallel: Your existing rankings (top 100 by traffic value from DataForSEO) Competitor keywords (top 200 per competitor) Seed expansion: Claude Haiku generates 30 seed phrases based on your ICP's pain points and integrations, then DataForSEO expands each seed into ~30 related keywords (30 parallel API calls), then bulk KD lookup on all of them That's roughly 2,000 raw keywords before dedup. After merging and deduplicating, it filters by volume floor, KD ceiling relative to your domain rating, and strips anything you already rank top-5 for Then Claude Haiku classifies every remaining keyword into TOFU/MOFU/BOFU in parallel batches of 50. Claude Sonnet groups them into 6–10 topical clusters. Each cluster gets a pillar keyword and supporting keywords. Opportunity scoring uses a weighted additive formula (not multiplicative since it compresses everything toward zero): score = (0.40 × log_volume + 0.40 × difficulty + 0.20 × funnel) × 100 Volume is log-normalized against a 100k anchor so a 1,000/mo keyword scores 60% instead of 1%. 70+ means actually worth targeting. It picks one topic per cluster (breadth-first), generates SEO titles for all 10, and saves them to your content pipeline. Step 3: Content Engine Per article, it runs: DataForSEO advanced SERP for the target keyword → Firecrawl scrapes the top 3 ranking articles to extract H2 structure and avg word count Tavily batch search: 3 queries in parallel for recent news, expert opinions, common mistakes YouTube Data API → transcript extraction via Apify → Claude Haiku pulls 2 concrete insights Then Claude Haiku does SERP gap analysis like what are all 3 top articles covering, what are they missing, what's the best featured snippet opportunity. Claude Sonnet generates a full outline: every H2, H3, word count per section, where research gets placed, where the product mention goes (with specific framing instruction), image positions, CTA matched to funnel stage. Then Claude Sonnet writes the full article in one shot against that outline. Images get generated after Haiku reads the actual written content to create better DALL-E prompts than you'd get from just the keyword. Schema markup and meta assets are separate Haiku calls. Product plug is deliberately constrained: one mention, at a designated section, only after the reader has felt the pain it solves. No marketing language. The outline specifies the exact framing. Output is a folder: article.md (pure content, copy into CMS), publish-kit.md (meta, schema JSON, publishing checklist), and images/. The whole thing is Claude Code slash commands - /blog-onboard, /blog-topics, /blog-write. You run them in any project directory. All data stays local. I open-sourced it here: github.com/maun11/claude-blog-engine It's working but honestly there's a lot of room to improve it. If you've built anything in this space or have opinions on the architecture, would genuinely appreciate the feedback. And if you improve something, PRs are welcome and there's a lot of low-hanging fruit in the pipeline script (scripts/topics_pipeline.py) specifically. submitted by /u/Visible-Mix2149 [link] [comments]

What is the best way to capture online mentions and capture signals daily?

I was talking with a friend who works in a startup and they would like to track decision makers across a specific set of companies and be updated daily. News mentions, comments or signals from those people across the web, and social media, particularly X. What is the best way to achieve this? A custom solution made with Claude code, leveraging scrapers (Ex. Firecrawl, Apify)? Or would n8n and similar platforms be better? Any personal experiences with this? Thanks! submitted by /u/W0rldIsMy0yster [link] [comments]