SeekamiLilian Weng @lilianweng
Lilian Weng ranks #21 in the public Seekami AI Influence Top 100 and is classified as Researcher.
Who is Lilian Weng?
Lilian Weng’s Lil’Log began as learning notes in 2017 and developed into a technical resource spanning reinforcement learning, generative models and language models. The articles connect concepts, papers and implementation concerns. Beyond a conventional biography, the distinctive public contribution is this body of long-form explanations and the research references readers can follow.
Notable work and what to look for
The 2023 article LLM Powered Autonomous Agents organizes agents around planning, memory and tool use, with discussion of decomposition and reflection. It is a useful architecture checklist: which capability comes from the model, and which from retrieval, code or an external service? Explicit components make a product easier to reason about than attributing every behavior to intelligence.
Interviews, work and further reading
These reading paths include interviews, first-person writing, institutional biographies and original research. Not every item is an interview.
- Lil’Log · Research notes ↗
Choose by topic; related series cover hallucination, reward and data quality.
lilianweng.github.io - LLM Powered Autonomous Agents · 2023 ↗
Start with the system overview, then examine planning, memory, tools and challenges.
lilianweng.github.io
Explore related work
Seekami public ranking snapshot
Public research summary
| Field | Result |
|---|---|
| Rank band | Top 25 |
| Public category | Researcher |
| Useful signal | Research and model direction |
Peer position and reproducible findings
| Comparison | Result |
|---|---|
| Position in the public Top 100 | #21 of 100 |
| Researcher category position | #5 of 34 |
| Composite vs category average | +3.79 points (category average 89.05) |
| Strongest signal this release | Authority · 98.43 |
These findings describe relative position within the published snapshot. They support screening and verification, not inference about private attributes or future campaign results.
How to interpret the scores
The composite combines normalized public signals. Authority describes position in the public AI relationship network; public reach reflects observable scale; recent engagement reflects available activity signals in this release. Scores support relative comparison and are not ratings of truth, personal ability or campaign performance.
Creators nearby in the ranking
Source and limitations
This page uses the formal Seekami ranking snapshot. Profiles and public signals change, and rank is not a third-party endorsement. Read the methodology and data boundaries.