Job tailoring advice is everywhere but it all bottlenecks at the same place: actually doing it 50 times. Copy resume, paste into ChatGPT, rewrite bullets, reformat everything that broke, repeat. Ajusta sits on the job page (LinkedIn, Indeed, etc.), reads the posting, and rewrites your resume against the JD without touching your formatting or adding anything that isn't already there. The output...
https://github.com/llmsresearch/paperbanana
The PaperBanana paper (arXiv:2601.23265) from Google Cloud AI Research and PKU describes a multi-agent framework for generating publication-ready academic illustrations from text. The official code hasn't been released yet, so I implemented it from the paper.The pipeline chains 5 agents: a Retriever that selects reference diagrams, a Planner that generates a textual description, a Stylist that...
https://github.com/llmsresearch/paperbanana
The original authors haven't released code yet, so I built it from the paper. It takes a methodology section as input and generates a publication-style diagram.The pipeline uses five agents: a retriever selects reference diagrams via in-context learning, a planner drafts the layout, a stylist adjusts for conference aesthetics, a visualizer renders with Gemini, and a critic evaluates and refines...
A few highlights: - Speculative Actions: parallel API execution, ~30% speedup - KVComm: share KV pairs instead of text, 30% of layers gets near-full performance - DoVer: intervention-driven debugging that flips 28% of failures to successes
Happy to discuss any of the papers or the framing. The decision matrix at the end maps each problem to a starting paper.
So I built a covering their capabilities. If you're evaluating agentic solutions for your next project, hope this saves you time. It maps what's native, what needs integration, and what's not supported.
Let me know if anything needs correction or want to add any other framework.