AI tool comparison
Career-Ops vs Gemini Deep Research API
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
Career-Ops
Claude Code agent that scans 45+ job portals and auto-generates ATS-optimized CVs
75%
Panel ship
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Community
Paid
Entry
Career-Ops is an open-source job search automation pipeline built on top of Claude Code. Created by indie developer santifer after getting laid off, it scans 45+ company career portals in parallel, scores each listing A–F across 10 weighted dimensions (tech stack match, growth stage, remote policy, etc.), and auto-generates tailored ATS-optimized PDF resumes for every application — all from a terminal dashboard. The creator used it personally to evaluate over 740 job listings, generate 100+ personalized CVs, and eventually land a Head of Applied AI role. The whole pipeline runs locally, with no SaaS fees or data sharing — just your API key and a YAML config for your preferences and skills. What makes Career-Ops stand out is the combination of deterministic scoring with AI-generated personalization. The scoring rubric is user-configurable, so you can weight "remote-first" heavily or prioritize Series B startups. Released April 4, 2026, it hit 21k GitHub stars within four days and is trending on Product Hunt today — a rare indie tool that solves a genuinely painful problem.
Developer Tools
Gemini Deep Research API
Autonomous research agents with MCP and native charts in your app
75%
Panel ship
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Community
Paid
Entry
Google opened its Deep Research and Deep Research Max agents to developers via the Gemini API, running on Gemini 3.1 Pro. These are the same autonomous research agents that power the consumer Gemini experience — now available as API primitives you can embed in your own apps, dashboards, or agentic workflows. Deep Research Max is benchmarked at 93.3% on DeepSearchQA, a record for autonomous research. The April 2026 API launch adds capabilities beyond the consumer product: MCP server support for connecting to private data and professional streams (FactSet, S&P Global, and PitchBook integrations are already live), native chart and infographic generation inline with research output, and the ability to mix sources simultaneously — web search, uploaded PDFs/CSVs/video/audio, and URL context. Code Execution and File Search also run alongside web grounding in a single call. For developers building research-heavy apps — competitive intelligence, financial analysis, legal research, scientific literature review — this is a meaningful unlock. Rather than chaining together search, retrieval, synthesis, and visualization layers yourself, the Deep Research API handles the full multi-hop research loop. Pricing and rate limits at enterprise scale remain the key question.
Reviewer scorecard
“This is exactly what Claude Code was made for — a high-signal agentic loop that replaces hours of manual work with a config file and a run command. The fact the creator used it to actually land a job makes it more credible than 90% of 'AI-powered' job tools. Fork it, tweak the scoring weights, ship your apps.”
“The MCP integration is the real story — connecting Deep Research to our internal data warehouse with a single server definition and getting research-grade synthesis in return is exactly what enterprise AI apps need. This replaces three separate pipeline stages for us.”
“Generating 100+ tailored resumes sounds impressive until you realize most ATS systems now flag mass-application patterns. If every laid-off dev runs this, recruiters will start seeing the same Claude-generated phrasing everywhere and discount it. Also, scraping 45 career portals at scale risks IP bans and ToS violations.”
“93.3% on DeepSearchQA sounds great until you hit domain-specific queries where benchmark performance rarely holds. With Google controlling the search layer, there are legitimate questions about source diversity and SEO-optimized results contaminating research quality.”
“The meta-narrative here is striking: AI displaced this developer, and then AI tools helped them land a better job. Career-Ops points toward a near future where your job search agent runs 24/7, continuously matching your evolving skill profile against a live stream of openings. The labor market is about to get very weird.”
“When every developer app embeds a research agent that simultaneously queries the live web and private data, the gap between Bloomberg Terminal-quality research and a startup's internal tool effectively collapses.”
“As someone who's spent days customizing resumes for specific roles, the idea of a local pipeline that generates polished PDFs tailored to each JD is genuinely appealing. The terminal dashboard aesthetic is very much dev-only right now, but if someone wraps a nice UI around this it becomes a serious Teal alternative.”
“Native chart generation inside research output is the killer feature — I can hand a client a report with visualizations baked in, not just text summaries. That changes the entire deliverable format for research-heavy creative work.”
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