Automate candidate sourcing and cut time-to-hire with AI recruiting software and sourcing tools built for busy talent teams.
Fetcher is praised for its effective automation in candidate sourcing, significantly reducing the time spent on recruitment tasks. Users appreciate its user-friendly interface and the accuracy of candidate matches. However, some users express concerns over occasional glitches and the need for more robust customer support. Pricing is viewed as reasonable, aligning well with the tool's capabilities, and Fetcher holds a generally positive reputation among recruitment professionals.
Mentions (30d)
0
Reviews
0
Platforms
2
Sentiment
0%
0 positive
Fetcher is praised for its effective automation in candidate sourcing, significantly reducing the time spent on recruitment tasks. Users appreciate its user-friendly interface and the accuracy of candidate matches. However, some users express concerns over occasional glitches and the need for more robust customer support. Pricing is viewed as reasonable, aligning well with the tool's capabilities, and Fetcher holds a generally positive reputation among recruitment professionals.
Features
Use Cases
Industry
information technology & services
Employees
140
Funding Stage
Series B
Total Funding
$41.2M
Pricing found: $379 /mo, $649 /mo
Built an AI flat-finder in a weekend. Indian rental sites are 70% broker spam so I scraped Reddit instead.
Weekend build, ~10 hours. Demo: https://trurent-five.vercel.app/ Problem I was poking at: every major Indian rental site (NoBroker, MagicBricks, 99acres) is infested with brokers even when you filter "direct owner." Reddit actually has honest listings posted by owners themselves but the posts are completely unsearchable. So I built TruRent. You chat with it, it parses the query into a structured search, runs it, the map updates live, and follow-ups carry context. Ask "compare the top two" and the model reasons over the actual listings instead of just filtering. Stack and the boring decisions: Next.js 16 with raw fetch to Anthropic. No SDK, I wanted full control of the streaming loop Claude Haiku 4.5, not Sonnet. The task doesn't need Sonnet and Haiku is 5x cheaper Two tools only (search, get_details). Comparison and ranking happen in the model's prose, not as separate tools. More tools = more failure modes NDJSON to the browser, way easier than parsing SSE Scrape pipeline: PullPush API to pull Reddit posts, then Haiku again to extract structured listings from raw post text, Nominatim for geocoding Honest numbers: 1,412 posts scraped, ~600 passed a local pre-filter, only 131 ended up being real listings. Dataset is tiny but the pipeline is source-agnostic, swap the fetcher and the rest doesn't change. Most curious about: anyone else built agents where they deliberately used fewer tools and let the model reason over richer tool outputs instead of adding more tools? Happy to get into any of it. submitted by /u/Scary-Alternative-81 [link] [comments]
View originalFree Premiere Pro extension to download YouTube/Instagram/X videos and auto-import to your project bin
Tired of the whole workflow — open browser, find video, download, wait, drag into Premiere. So I built a panel that does it all from inside Premiere. Paste a URL, pick quality, click Download & Import — file lands directly in your project bin. Supports: - YouTube, Instagram, X/Twitter - Best / 1080p / 720p / 480p / MP3 - Live progress bar with ETA - Auto-import to project bin on finish Powered by yt-dlp under the hood. Built this with the help of Claude (AI) — had the idea and kept iterating until it worked. Free & open source: https://github.com/gitttsarya/media-fetcher-premiere Full install guide in the README. Let me know if you run into any issues! https://preview.redd.it/y5svnpjuoj1h1.png?width=482&format=png&auto=webp&s=0dfb391daefb06dbbb37c4b13174efc6a1ca7a22 submitted by /u/CounterCultural6967 [link] [comments]
View originalSharing a beginner-friendly orchestration workflow for anyone just getting started building with Claude Code.
It demonstrates the Command → Agent → Skill pattern end-to-end: a /weather-orchestrator command asks the user for °C or °F, invokes a weather-agent (which uses a preloaded agent skill to fetch live temperature from Open-Meteo), then calls a separate weather-svg-creator skill that renders an SVG weather card and writes an output file. It intentionally shows both skill patterns side-by-side — preloaded agent skills vs. directly-invoked skills via the Skill tool, so you can see when to reach for each. The whole repo (and this workflow) was built using Claude Code itself. Good starting point if you're figuring out how commands, subagents, and skills fit together before building something bigger. Repo: https://github.com/shanraisshan/claude-code-best-practice submitted by /u/shanraisshan [link] [comments]
View originalPricing found: $379 /mo, $649 /mo
Key features include: Talent sourcing, Candidate engagement, Smart recruitment analytics, A high-quality talent pool, Flexible talent acquisition options, Personalized diversity search criteria, Integrations are a walk in the park, Ambitious companies trust Fetcher.
Fetcher is commonly used for: Streamlining the recruitment process for tech startups, Enhancing candidate engagement through personalized outreach, Building diverse talent pipelines for inclusive hiring, Utilizing smart analytics to improve sourcing strategies, Reducing time-to-hire for high-demand roles, Facilitating remote hiring for global talent acquisition.
Fetcher integrates with: LinkedIn, Greenhouse, Lever, Workable, BambooHR, Zapier, Slack, Google Workspace, Microsoft Teams, Jobvite.
