Keeping up with AI these days feels like drinking from a firehose.
Research papers drop daily. New models launch weekly. Policy debates evolve in real-time. For researchers, developers, and product managers who need to stay informed, the sheer volume has crossed from exciting to overwhelming.
You know the routine: set aside Sunday morning to catch up. Open twenty tabs across arXiv, GitHub, news sites, and policy blogs. Three hours later, you’re skimming abstracts, bookmarking articles you’ll never read, and still feeling like you’re missing something critical.
The Scale of the Problem
The data makes it clear: information overload is a very real challenge. From January 2025 through February 8, 2026, arXiv alone published 115,471 AI papers over 100+ categories. That’s roughly 275 papers per day, with June 2025 peaking at 11,664 papers in a single month. The top categories were Machine Learning (27,762), Computer Vision (27,358), and NLP (17,616), while trending keywords included “learning” (49K+), “large language model” (27K+), and “architecture” (17K+).
This snapshot is drawn from another project, which tracks AI-related papers published daily on arXiv to provide up-to-date insights into research trends and volume.
And that’s just research. Add in daily tool releases, breaking industry news, and evolving debates on AI safety, and you’ve got a perfect storm of too much information, too little time.
This is the problem I built the AI Landscape Monitor to solve.
Automated Research Assistant
The AI Landscape Monitor serves as a dedicated research assistant, ready to provide support whenever you need it. For example, you can schedule it to run weekly and receive a concise summary every Monday morning.
This web-based platform harnesses the power of both large language models and traditional search tools to automatically collect, distill, and organize the latest developments into clear, easy-to-read HTML digests.
A 360° View: Four Essential AI Digest Sections
The AI Pulse Weekly, hosted on UNU’s intranet and powered by this engine, was created to deliver timely AI updates to the UNU community.
Every digest covers four key dimensions:
- Research Highlights: Novel papers with meaningful contributions
- News & Trends: Industry updates, model releases, partnerships, policy shifts
- Tools & Resources: Open-source projects, datasets, frameworks
- Perspectives & Ethics: AI safety, policy debates, societal impact
Choose your AI engine per section or use one across the board. Want Claude for research papers and Perplexity for tools? Done.
How It Works
1. Configure — Select your AI engine, customize prompts for specific topics, add manual links if needed
2. Generate — The engine executes queries, gathers data, summarizes findings, formats citations
3. Receive — Get a curated HTML digest ready to distribute or archive
What used to take hours now takes minutes.
Built for Team Sharing
The return on investment becomes particularly compelling when the digest is generated once and distributed throughout the organization. The HTML output is fully self-contained and ready for dissemination:
- Publish it on the internal wiki
- Circulate it via weekly email briefings
- Integrate it into the intranet
- Archive it for future reference
One person configures and generates. The entire organization stays current. That’s force multiplication.
Beyond AI
The tool isn’t limited to AI news. Customize the prompts for any field drowning in information:
- E-government and digital infrastructure
- GovTech innovations
- Cybersecurity trends
- Climate tech developments
- Any domain you need to track
Same engine, different focus areas.
Automation-Ready
For workflow automation enthusiasts, there’s a REST API. Schedule it weekly, pipe the output to your team, and integrate it into existing workflows.
What’s Next?
This is an active research project. The roadmap focuses on high-value features:
- Scheduled Reports — Automate generation and delivery on custom schedules
- Custom Templates — Save and reuse your favorite configurations
Additional features like Report Composer (for mixing sections from multiple reports, historical or same-day) are under consideration based on community feedback.
On the horizon: Multi-Engine Synthesis, automatically run a topic through all search engines and synthesize into one unified digest with duplicates removed. An evolution of the Report Composer concept.
Project Documentation
See the Documentation for details: c3.unu.edu/projects/ai/landscapemonitor
GitHub: Coming soon