source:
[person] (@[person]). base in Bristol, UK; works with University of Bristol.

Climate, glaciers, and programming.

Representative projects and topics: Open Global Glacier Model; Add geolocalised subsetting, masking, and plotting operations to xarray; N-D labeled arrays and datasets in Python; Lectures notes of the scientific programming course at the University of Innsbruck; Educational material about glaciers, powered by OGGM and MyBinder; Class activities and notebooks for the MSc "Quantifying Climate Risks" class taught at Uni Bristol.

c00:
[person] (@[person]). base in Hamburg, Germany.

Indie product engineer who loves to build tools for developers.

Representative projects and topics: The infamous DuckDB quack extension rewritten in C and built with Zig; A Rust library to decrypt & encrypt any cheat code for CodeBreaker PS2; Serverless app to keep track of your Twitter followers and unfollowers; Enjoy Dilbert in your RSS feed reader without any ads; The fastest way to run Chef cookbooks; Shell library to test your tools like Git does.

c01:
[person] (@[person]). base in Berkeley.

🏍 🛣 🌎 I miss _why.

Representative projects and topics: Give your JS App some Backbone with Models, Views, Collections, and Events; JavaScript's utility _ belt; Unfancy JavaScript; Literate Programming can be Quick and Dirty.; Code as Art, Art as Code. Processing and Ruby are meant for each other.; A TextMate Bundle for CoffeeScript.

{"message":"Moved Permanently","url":"

c04:
[person] (@[person]). base in San Francisco, CA; works with CEO @ Ping.gg.

I'm just here for the vibes, man

Representative projects and topics: The best way to start a full-stack, typesafe Next.js app; How to implement Stripe without going mad; File uploads for modern web devs; A fast, local-first "search engine" for !bang users; Export Twitch markers as a LosslessCut-compatible CSV; Turn SVGs into high resolution PNGs in 2 clicks.

Current Projects

Products

T3 Chat** — The only AI chat that doesn't suck
T3 Code** — A harness for your AI harnesses
UploadThing** — File uploads for modern web devs (5k+ stars)
QuickPic** — Turn SVGs into high resolution PNGs in 2 clicks (1.2k+ stars)
Unduck** — Fast, local-first search engine for !bang users (1.1k+ stars)
Ping.gg** — Zoom for streamers, built for OBS
PicThing** — Background removal and asset management for professional creators
Shoo** — Simple auth (coming soon)
Lawn** — Open source alternative to Frame.io

LLM Benchmarks & Shenanigans

SnitchBench** — Measuring which LLMs will snitch on you
Skatebench** — Measuring how well LLMs can name skateboard tricks
Quipslop** — Quiplash for LLMs, streaming on Twitch

Everything Else

T3 Stack** — Ushered in full stack typesafety with create-t3-app (28k+ stars)
Stripe Recommendations** — How to implement Stripe without going mad (6.2k+ stars)
Claude Blocker** — Block distracting apps when you don't have agents running
Soramarker** — Add Sora watermarks to any video [TRUNCATED]

c05: YC batch S2020 founder at Humanloop. Role: Founder. Ex-MonolithAI and Google. ML PhD @ UCL and MSc in Physics @ Cambridge.

c06:
[person] (@[person]). works with @bentoml.

emacs otaku

Representative projects and topics: Hua (㕦) is a hy-like lisp language to lua compiler.; [zsy.ds4] is is a simple max patch to handle Dualshock 4 inputs (over bluetooth); retro forth in lua; lightweight script for nanopi oled hat; toy dsp library in python; The Modular Platform (includes MAX & Mojo).

c07: Public author profile is associated with Innsbruck Medical University. Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. An autonomous agent that plans and runs de novo drug-design iterations, validated against real wet-lab results. OpenAlex concept tags include "medicine, anesthesia, intensive care medicine, surgery '.

c08: YC batch W2024 founder at Ecliptor. Role: Founder. Co-founder & CEO of Ecliptor. Machine Intelligence at the University of Toronto, deep learning research, publishing 4 papers in CVPR, ICCV and NeurIPS. Previously product at Microsoft, and ML at Twitch.

c09: Public author profile is associated with University of Padua. Recent work in computational biology includes 'Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction'. Finds that sampling the loss landscape at intermediate Langevin-dynamics temperatures best trains transformers for protein structure prediction. OpenAlex concept tags include "microstructure, materials science, metallurgy, chemistry '.

c10: Public author profile is associated with Hong Kong Polytechnic University. Recent work in computer vision includes 'OmniRoam: World Wandering via Long-Horizon Panoramic Video Generation'. Generates long-horizon, navigable panoramic video to model persistent, explorable 3D scenes. OpenAlex concept tags include "materials science, microstructure, composite material, metallurgy '.

c12: Public author profile is associated with Tianjin University of Technology. Recent work in computer vision includes 'OmniRoam: World Wandering via Long-Horizon Panoramic Video Generation'. Generates long-horizon, navigable panoramic video to model persistent, explorable 3D scenes. OpenAlex concept tags include "chemistry, materials science, chemical engineering, physics '.

c13: Public author profile is associated with University of Wollongong. Recent work in ai and machine learning includes 'Aligning Validation with Deployment: Target-Weighted Cross-Validation for Spatial Prediction'. Proposes a target-weighted cross-validation scheme so spatial-prediction error estimates match the actual deployment distribution. OpenAlex concept tags include "medicine, mathematics, statistics, econometrics '.

c14: Public author profile is associated with Minzu University of China. Recent work in computer vision includes 'OmniRoam: World Wandering via Long-Horizon Panoramic Video Generation'. Generates long-horizon, navigable panoramic video to model persistent, explorable 3D scenes. OpenAlex concept tags include "chemistry, medicine, computer science, artificial intelligence '.

c16: Public author profile is associated with Friedrich-Alexander-Universität Erlangen-Nürnberg. Recent work in quantitative social science and economics includes 'Retrospective Economic Evaluation of Group Testing in the COVID-19 Pandemic'. Retrospectively evaluates the economic cost-effectiveness of pooled group testing as COVID-19 surveillance policy. OpenAlex concept tags include "medicine, computer science, mathematics, biology '.

c17: Public author profile is associated with University of Michigan–Ann Arbor. Recent work in quantitative social science and economics includes 'The Problem of Dynamic Spatial Sampling and Geofence Surveillance'. Formalizes the dynamic spatial-sampling problem underlying geofence-based surveillance systems. OpenAlex concept tags include "replication (statistics), computer science, environmental science, engineering '.

c18: Public author profile is associated with Technological University Dublin. Recent work in ai and machine learning includes 'Meteorology-Driven GPT4AP: A Multi-Task Forecasting LLM for Atmospheric Air Pollution in Data-Scarce Settings'. A multi-task LLM that uses meteorological inputs to forecast air pollution in regions with sparse monitoring data. OpenAlex concept tags include "computer science, artificial intelligence, geography, engineering '.

c19: Public author profile is associated with Folkwang University of the Arts. Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. An autonomous agent that plans and runs de novo drug-design iterations, validated against real wet-lab results. OpenAlex concept tags include "cosmic ray, physics, air shower, nuclear physics '.

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c00：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

criteria: 没有具体关联 只有泛泛的领域相似 有具体共同方向或可用的互补能力 有非常明确的共同工作内容或直接互补

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c01：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c02：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c03：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c04：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c05：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c06：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c07：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c08：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c09：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c10：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c11：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c12：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c13：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c14：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c15：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c16：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c17：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c18：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c19：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补


source:
[person] (@[person]). base in Amsterdam; works with DuckDB Labs.

Mostly DuckDB

Representative projects and topics: DuckDB is an analytical in-process SQL database management system; New Parser for DuckDB; DuckDB is an embeddable SQL OLAP Database Management System; Library to read a subset of Parquet files; duckdb extension for curl; Website [person].

c00:
[person] (@[person]). works with @BentoML.

Creating tech content

Representative projects and topics: Unified Model Serving Framework 🍱; Everything you need to know about LLM inference; Claude Code 可运行版; Typescript 类型全修复;企业级可靠性; 安全无毒, lock 文件保真, 可直接 bun i; bun run dev 启动; Sherlock's personal website; Operating LLMs in production; The Modular Platform (includes MAX & Mojo).

Hi there 👋, I am Sherlock.

🔭 I’m currently working on technical content.
Docs, blog posts, case studies, etc. @BentoML.
Maintaining LLM Inference Handbook.
🌱 I’m currently learning AI, machine learning, Kubernetes, and Python.
💬 Ask me about:
Technical content, like docs and blog posts.
Detective novels.
😄 Pronouns: He/His/Him.
⚡ Fun facts:
Big fan of detective novels.
Favorite detectives: Sherlock Holmes, Hercule Poirot, Ellery Queen, Dr. Gideon Fell.
Favorite authors: Arthur Conan Doyle, Agatha Christie, John Dickson Carr.

c01:
[person] (@[person]). base in Brooklyn, NY; works with Citadel.

Turning coffee into software. Formerly developer tooling at @replayio, React core at @facebook, and @google.

Representative projects and topics: Simple reusable React error boundary component; React components for efficiently rendering large lists and tabular data; React slideshow framework; Standalone version of the AutoSizer component from react-virtualized; InfiniteLoader component inspired by react-virtualized but for use with react-window; Redux bindings for client-side search.

I make music (soundcloud.com/brianvaughn) and software (github.com/[person]).

If you like my work, 🎉 become a sponsor or ☕ buy me a coffee

c02:
[person] (@[person])

Representative projects and topics: Sampling CPU profiler for Ruby; Recipes for using Python's pandas library; spy on the DNS queries your computer is making; Source for Mess With DNS; simple markdown translator; twine.

hello!

The main repositories here are a Ruby profiler I wrote called rbspy and a cookbook for learning pandas. There are also dozens of other tiny toy projects that I made for fun once and don't maintain at all.

Some other places you can find my work on the internet:

blog:
zines:

c03:
[person] (@[person]). base in San Francisco, CA.

Founder/CEO of BentoML, previously @databricks, ML/AI Platforms & Systems, Product Design

Representative projects and topics: The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!; Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud.; Applying Genetic Algorithm to Travelling Salesman Problem; Unified Model Serving Framework; Benchmark and optimize LLM inference across frameworks with ease; Everything you need to know about LLM inference.

Hi there 👋

🔭 I’m currently working on 🍱 BentoML - 🙋 We're hiring!
🤖 Ask me about building ML/AI applications
📷 Photography: Eyes of San Francisco

c04:
[person] (@[person]). base in Berkeley.

🏍 🛣 🌎 I miss _why.

Representative projects and topics: Give your JS App some Backbone with Models, Views, Collections, and Events; JavaScript's utility _ belt; Unfancy JavaScript; Literate Programming can be Quick and Dirty.; Code as Art, Art as Code. Processing and Ruby are meant for each other.; A TextMate Bundle for CoffeeScript.

{"message":"Moved Permanently","url":"

c05: Public author profile is associated with Council of Agriculture. Recent work in ai and machine learning includes 'Reward-Based Online LLM Routing via NeuralUCB'. Uses a NeuralUCB bandit algorithm to route queries among LLMs online for cost-aware reward optimization. OpenAlex concept tags include "irrigation, water resource management, environmental science, engineering '.

c07: Public author profile is associated with Technological University Dublin. Recent work in ai and machine learning includes 'Meteorology-Driven GPT4AP: A Multi-Task Forecasting LLM for Atmospheric Air Pollution in Data-Scarce Settings'. A multi-task LLM that uses meteorological inputs to forecast air pollution in regions with sparse monitoring data. OpenAlex concept tags include "computer science, artificial intelligence, geography, engineering '.

c08: Public author profile is associated with Technological University Dublin. Recent work in ai and machine learning includes 'Meteorology-Driven GPT4AP: A Multi-Task Forecasting LLM for Atmospheric Air Pollution in Data-Scarce Settings'. A multi-task LLM that uses meteorological inputs to forecast air pollution in regions with sparse monitoring data. OpenAlex concept tags include "computer science, artificial intelligence, machine learning, engineering '.

c09: Public author profile is associated with University College Dublin. Recent work in ai and machine learning includes 'Meteorology-Driven GPT4AP: A Multi-Task Forecasting LLM for Atmospheric Air Pollution in Data-Scarce Settings'. A multi-task LLM that uses meteorological inputs to forecast air pollution in regions with sparse monitoring data. OpenAlex concept tags include "computer science, artificial intelligence, environmental science, meteorology '.

c10: Public author profile is associated with Antea Group (France). Recent work in ai and machine learning includes 'Aligned, Orthogonal or In-conflict: When can we safely optimize Chain-of-Thought?'. Studies when optimizing an LLM's visible chain-of-thought stays a faithful monitor of its reasoning versus becomes misleading. OpenAlex concept tags include "medicine, biology, genetics, immunology '.

c11: Public author profile is associated with Alibaba Group (United States). Recent work in ai and machine learning includes 'Think Anywhere in Code Generation'. Lets a reasoning LLM interleave thinking throughout code generation instead of only reasoning upfront. OpenAlex concept tags include "computer science, artificial intelligence, physics, programming language '.

c12: Public author profile is associated with Massachusetts Institute of Technology. Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. An autonomous agent that plans and runs de novo drug-design iterations, validated against real wet-lab results. OpenAlex concept tags include "rna, biology, computational biology, gene '.

c13: Public author profile is associated with Yeshiva University. Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. An autonomous agent that plans and runs de novo drug-design iterations, validated against real wet-lab results. OpenAlex concept tags include "medicine, computer science, physics, optics '.

c14: Public author profile is associated with Folkwang University of the Arts. Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. An autonomous agent that plans and runs de novo drug-design iterations, validated against real wet-lab results. OpenAlex concept tags include "cosmic ray, physics, air shower, nuclear physics '.

c15: Public author profile is associated with University of Twente. Recent work in computer vision includes 'SurgNavAR: An Augmented Reality Surgical Navigation Framework for Optical See-Through Head Mounted Displays'. An augmented-reality surgical navigation system that overlays preoperative 3D imaging via optical see-through headsets. OpenAlex concept tags include "augmented reality, sensory cue, psychology, gait '.

c16: Public author profile is associated with Kunming University of Science and Technology. Recent work in computational biology includes 'A Hierarchical Sheaf Spectral Embedding Framework for Single-Cell RNA-seq Analysis'. Uses sheaf-theoretic spectral embeddings to represent single-cell RNA-seq structure across multiple scales in a stable way. OpenAlex concept tags include "chemistry, medicine, physics, biology '.

c17: Recent work in computer vision includes 'Video Models Reason Early: Exploiting Plan Commitment for Maze Solving'. Finds video diffusion models commit to a maze-solving plan early in generation, exploited to speed up reasoning. OpenAlex concept tags include "computer science, artificial intelligence, machine learning, task (project management) '.

c18: Public author profile is associated with Google DeepMind (United Kingdom). Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. An autonomous agent that plans and runs de novo drug-design iterations, validated against real wet-lab results. OpenAlex concept tags include "computer science, artificial intelligence, machine learning, biology '.

c19: Public author profile is associated with University of California, Berkeley. Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. An autonomous agent that plans and runs de novo drug-design iterations, validated against real wet-lab results. OpenAlex concept tags include "biology, computational biology, cancer, gene '.

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c00：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

criteria: 没有具体关联 只有泛泛的领域相似 有具体共同方向或可用的互补能力 有非常明确的共同工作内容或直接互补

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c01：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c02：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c03：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c04：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c05：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c06：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c07：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c08：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c09：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c10：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c11：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c12：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c13：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c14：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c15：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c16：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c17：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c18：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c19：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补


source:
[person] (@[person]). base in SF & Utah; works with LangChain.

honey I think we left the k8s autoscaler on

Representative projects and topics: The agent engineering platform; Framework to build resilient language agents as graphs.; Deep Agents in JS; Declarative streams for Typescript; 🦤 My personal website; The AI Toolkit for TypeScript. From the creators of Next.js, the AI SDK is a free open-source library for building AI-powered applications and agents.

c00:
[person] (@[person]). base in Catania, Sicily, Italy; works with Redis Labs.

Computer programmer based in Sicily, Italy. I mostly write OSS software. Born 1977. Not a puritan.

Representative projects and topics: A radix tree implementation in ANSI C; Dump1090 is a simple Mode S decoder for RTLSDR devices; A small self-contained alternative to readline and libedit; A text editor in less than 1000 LOC with syntax highlight and search.; Simple Dynamic Strings library for C; Disque is a distributed message broker.

c01:
[person] (@[person]). base in Bristol, UK; works with University of Bristol.

Climate, glaciers, and programming.

Representative projects and topics: Open Global Glacier Model; Add geolocalised subsetting, masking, and plotting operations to xarray; N-D labeled arrays and datasets in Python; Lectures notes of the scientific programming course at the University of Innsbruck; Educational material about glaciers, powered by OGGM and MyBinder; Class activities and notebooks for the MSc "Quantifying Climate Risks" class taught at Uni Bristol.

c02:
[person] (@[person]). base in Hamilton, Ontario; works with me.

I create courses that make learning things like JavaScript easy and fun 🔥

Representative projects and topics: Slam Dunk JavaScript; 30 Day Vanilla JS Challenge; Starter files for learning React.js with React for Beginners; Ask me anything!; Starter Files and Solutions for Full Stack Advanced React and GraphQL; dad jokes.

[person]

Waku website

Running Locally

`pnpm install` then `pnpm run dev`.

If it's your first time, you'll need to run `pnpm run db:migrate` to generate the schema for the hot tips posts data.

c03:
[person] (@[person]). base in Sacramento; works with Mathematics and Statistics Department, CSU Sacramento.

Statistics Professor @ Sacramento State

Representative projects and topics: Unmaintained scripts; Utility scripts, python modules, and dotfiles to be stored in home folder; Log of research activities for PhD in statistics; Transform Serial R Code into Parallel R Code; course materials for UC Davis STA141C: Big Data & High Performance Statistical Computing; running examples on slurm cluster.

c04:
[person] (@[person]). base in Amsterdam; works with DuckDB Labs.

Mostly DuckDB

Representative projects and topics: DuckDB is an analytical in-process SQL database management system; New Parser for DuckDB; DuckDB is an embeddable SQL OLAP Database Management System; Library to read a subset of Parquet files; duckdb extension for curl; Website [person].

c05: YC batch S2023 founder at Automorphic. Role: Founder. Building AI agents that can remember and self-improve at Automorphic.

c06: Public author profile is associated with Anhui University. Recent work in ai and machine learning includes 'Think Anywhere in Code Generation'. Lets a reasoning LLM interleave thinking throughout code generation instead of only reasoning upfront. OpenAlex concept tags include "computer science, programming language, artificial intelligence, software engineering '.

c07: Public author profile is associated with Alibaba Group (United States). Recent work in ai and machine learning includes 'Think Anywhere in Code Generation'. Lets a reasoning LLM interleave thinking throughout code generation instead of only reasoning upfront. OpenAlex concept tags include "computer science, artificial intelligence, physics, programming language '.

c08: YC batch S2023 founder at Automorphic. Role: Founder. When not working on Automorphic, building AI to beat me at chess.

c09:
[person] (@[person]). base in Amsterdam.

Software Engineer

Representative projects and topics: Template for DuckDB extensions to help you develop, test and deploy a custom extension; DuckDB is an in-process SQL OLAP Database Management System; duckdb-go provides a database/sql driver for the DuckDB database engine.; for testing purposes; intended for testing conflicting extension loads / functions; DuckLake is an integrated data lake and catalog format.

Charlotte (Lotte) Felius

Publications

G-ALP: Rethinking Light-weight Encodings for GPUs at DaMoN @ SIGMOD 2025
VCrypt: Leveraging Vectorized and Compressed Execution for Client-Side Encryption at EDBT 2025
Enhancing Security for Columnar Storage and Data Lakes at VLDB 2024
DuckDB-SGX2: The Good, The Bad and The Ugly within Confidential Analytical Query Processing at DaMoN @ SIGMOD 2024
Accelerating GPU Data Processing using FastLanes Compression at DaMoN @ SIGMOD 2024

Education
PhD Student at CWI in the Database Architectures Group working on secure query processing
MSc Computational Science (Joint Degree) at University of Amsterdam & Vrije Universiteit Amsterdam
BSc Systems Engineering, Policy Analysis & Management at Delft University of Technology

c10: Public author profile is associated with Council of Agriculture. Recent work in ai and machine learning includes 'Reward-Based Online LLM Routing via NeuralUCB'. Uses a NeuralUCB bandit algorithm to route queries among LLMs online for cost-aware reward optimization. OpenAlex concept tags include "irrigation, water resource management, environmental science, engineering '.

c11: Public author profile is associated with Gansu Agricultural University. Recent work in ai and machine learning includes 'Think Anywhere in Code Generation'. Lets a reasoning LLM interleave thinking throughout code generation instead of only reasoning upfront. OpenAlex concept tags include "medicine, chemistry, biology, internal medicine '.

c12: Public author profile is associated with Peking University. Recent work in ai and machine learning includes 'Think Anywhere in Code Generation'. Lets a reasoning LLM interleave thinking throughout code generation instead of only reasoning upfront. OpenAlex concept tags include "computer science, artificial intelligence, programming language, software engineering '.

c13: Public author profile is associated with Folkwang University of the Arts. Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. An autonomous agent that plans and runs de novo drug-design iterations, validated against real wet-lab results. OpenAlex concept tags include "cosmic ray, physics, air shower, nuclear physics '.

c14: Public author profile is associated with Massachusetts Institute of Technology. Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. An autonomous agent that plans and runs de novo drug-design iterations, validated against real wet-lab results. OpenAlex concept tags include "rna, biology, computational biology, gene '.

c15: YC batch W2024 founder at Sonauto. Role: Founder. Previously a software engineering intern at Vocode (YC W23) and an intern on the Machine Learning Team at Ada.

c16: Public author profile is associated with Microsoft Research (United Kingdom). Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. An autonomous agent that plans and runs de novo drug-design iterations, validated against real wet-lab results. OpenAlex concept tags include "computer science, artificial intelligence, machine learning, mathematics '.

c17: Public author profile is associated with Exscien (United States). Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. An autonomous agent that plans and runs de novo drug-design iterations, validated against real wet-lab results. OpenAlex concept tags include "computer science, graph, artificial intelligence, machine learning '.

c18: Public author profile is associated with Xiamen University. Recent work in ai and machine learning includes 'Think Anywhere in Code Generation'. Lets a reasoning LLM interleave thinking throughout code generation instead of only reasoning upfront. OpenAlex concept tags include "chemistry, materials science, condensed matter physics, physics '.

c19: Public author profile is associated with Yeshiva University. Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. An autonomous agent that plans and runs de novo drug-design iterations, validated against real wet-lab results. OpenAlex concept tags include "medicine, computer science, physics, optics '.

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c00：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

criteria: 没有具体关联 只有泛泛的领域相似 有具体共同方向或可用的互补能力 有非常明确的共同工作内容或直接互补

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c01：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c02：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c03：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c04：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c05：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c06：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c07：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c08：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c09：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c10：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c11：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c12：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c13：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c14：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c15：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c16：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c17：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c18：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c19：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补


source:
[person] (@[person]). base in Washington, DC; works with @PrefectHQ.

CEO at @PrefectHQ. Building FastMCP. Mostly harmless.

Representative projects and topics: 🚀 The fast, Pythonic way to build MCP servers and clients.; Prefect is a workflow orchestration framework for building resilient data pipelines in Python.; an ambient intelligence library; 📋💬 Simple code-to-context utility; Generate images with hidden text; 🌴 Simple utility for managing parallel Claude Code instances.

Hello 👋

I'm Jeremiah.

I love building tools that make developers' lives easier. You might know me from FastMCP, the standard Python framework for working with the Model Context Protocol, or Prefect, the workflow engine used by thousands of data teams worldwide.

`$ whoami`

🏢 Founder & CEO at Prefect
📍 Based in Washington, DC
🎯 Strategic advisor to Spotify, FabFitFun, and Positive Sum
📊 Former Apache Airflow PMC member
☕ Global Ambassador for Compass Coffee
🌎 Mostly Harmless

`$ ping`

💻 [person].dev

c00:
[person] (@[person]). base in Hamilton, Ontario; works with me.

I create courses that make learning things like JavaScript easy and fun 🔥

Representative projects and topics: Slam Dunk JavaScript; 30 Day Vanilla JS Challenge; Starter files for learning React.js with React for Beginners; Ask me anything!; Starter Files and Solutions for Full Stack Advanced React and GraphQL; dad jokes.

[person]

Waku website

Running Locally

`pnpm install` then `pnpm run dev`.

If it's your first time, you'll need to run `pnpm run db:migrate` to generate the schema for the hot tips posts data.

c01:
[person] (@[person]). base in Stanford.

I like to train Deep Neural Nets on large datasets.

Representative projects and topics: The simplest, fastest repository for training/finetuning medium-sized GPTs.; The best ChatGPT that $100 can buy.; LLM training in simple, raw C/CUDA; Inference Llama 2 in one file of pure C; A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API; AI agents running research on single-GPU nanochat training automatically.

I like deep neural nets.

c02:
[person] (@[person]). base in Amsterdam; works with DuckDB Labs.

Mostly DuckDB

Representative projects and topics: DuckDB is an analytical in-process SQL database management system; New Parser for DuckDB; DuckDB is an embeddable SQL OLAP Database Management System; Library to read a subset of Parquet files; duckdb extension for curl; Website [person].

c03:
[person] (@[person]). works with @bentoml.

emacs otaku

Representative projects and topics: Hua (㕦) is a hy-like lisp language to lua compiler.; [zsy.ds4] is is a simple max patch to handle Dualshock 4 inputs (over bluetooth); retro forth in lua; lightweight script for nanopi oled hat; toy dsp library in python; The Modular Platform (includes MAX & Mojo).

c05:
[person] (@[person]). base in Amsterdam.

Software Engineer

Representative projects and topics: Template for DuckDB extensions to help you develop, test and deploy a custom extension; DuckDB is an in-process SQL OLAP Database Management System; duckdb-go provides a database/sql driver for the DuckDB database engine.; for testing purposes; intended for testing conflicting extension loads / functions; DuckLake is an integrated data lake and catalog format.

Charlotte (Lotte) Felius

Publications

G-ALP: Rethinking Light-weight Encodings for GPUs at DaMoN @ SIGMOD 2025
VCrypt: Leveraging Vectorized and Compressed Execution for Client-Side Encryption at EDBT 2025
Enhancing Security for Columnar Storage and Data Lakes at VLDB 2024
DuckDB-SGX2: The Good, The Bad and The Ugly within Confidential Analytical Query Processing at DaMoN @ SIGMOD 2024
Accelerating GPU Data Processing using FastLanes Compression at DaMoN @ SIGMOD 2024

Education
PhD Student at CWI in the Database Architectures Group working on secure query processing
MSc Computational Science (Joint Degree) at University of Amsterdam & Vrije Universiteit Amsterdam
BSc Systems Engineering, Policy Analysis & Management at Delft University of Technology

c06: YC batch S2024 founder at Laminar. Role: Founder. Co-founder and CEO @ Laminar (lmnr.ai). Previously interned at Palantir building semantic search and at Bloomberg scaling market tick processing pipeline by 10x to 10M ticks/s.

c07: YC batch W2026 founder at Captain. Role: CEO/Co-Founder. CEO @ Captain, Prev. IEEE, Founded CyberSpace (CTF hacking group), Recognized by the U.S. Congress for leadership in community software projects, Avid designer, Dropped out of Purdue

c08: Public author profile is associated with Adobe Systems (United States). Recent work in computer vision includes 'OmniRoam: World Wandering via Long-Horizon Panoramic Video Generation'. Generates long-horizon, navigable panoramic video to model persistent, explorable 3D scenes. OpenAlex concept tags include "computer science, artificial intelligence, computer vision, computer graphics (images) '.

c09: YC batch W2024 founder at Ecliptor. Role: Founder. Co-founder & CEO of Ecliptor. Machine Intelligence at the University of Toronto, deep learning research, publishing 4 papers in CVPR, ICCV and NeurIPS. Previously product at Microsoft, and ML at Twitch.

c10: YC batch S2024 founder at Laminar. Role: Co-founder and CTO. Previously worked at Amazon for 2 years building and scaling critical payments infrastructure. Spent a year creating ML infrastructure for a drug discovery biotech startup in Korea.

c11: YC batch W2023 founder at Resend. Role: Founder & CEO. Previously VP of Developer Experience at WorkOS and CPO at Liferay Cloud. Originally from Brazil, now living in San Francisco.

c12: YC batch W2024 founder at Tracecat. Role: Co-Founder and CTO. Previously worked on various observability, data and AI infrastructure teams at AMD, Meta, and a London-based quant trading firm. Bachelors and Masters degree in Computer Engineering from Imperial College London.

c13: Public author profile is associated with Minzu University of China. Recent work in computer vision includes 'OmniRoam: World Wandering via Long-Horizon Panoramic Video Generation'. Generates long-horizon, navigable panoramic video to model persistent, explorable 3D scenes. OpenAlex concept tags include "chemistry, medicine, computer science, artificial intelligence '.

c14: Public author profile is associated with Monash University Malaysia. Recent work in computer vision includes 'OmniRoam: World Wandering via Long-Horizon Panoramic Video Generation'. Generates long-horizon, navigable panoramic video to model persistent, explorable 3D scenes. OpenAlex concept tags include "computer science, artificial intelligence, natural language processing, machine learning '.

c15: Public author profile is associated with Princeton University. Recent work in computer vision includes 'Video Models Reason Early: Exploiting Plan Commitment for Maze Solving'. Finds video diffusion models commit to a maze-solving plan early in generation, exploited to speed up reasoning. OpenAlex concept tags include "computer science, artificial intelligence, machine learning, mathematics '.

c16: Public author profile is associated with Folkwang University of the Arts. Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. An autonomous agent that plans and runs de novo drug-design iterations, validated against real wet-lab results. OpenAlex concept tags include "cosmic ray, physics, air shower, nuclear physics '.

c17: YC batch S2025 founder at Modelence. Role: Co-Founder & CEO. Before Modelence, I co-founded CodeSignal (valued $500M) and spent a decade on building and scaling one of the largest skill assessment platforms, used by millions of users. Forbes 30u30 US & Canada, Enterprise Technology. Silver medalist of IOI 2007.

c18: Public author profile is associated with The University of Texas at Austin. Recent work in computational biology includes 'Poisoning the Genome: Targeted Backdoor Attacks on DNA Foundation Models'. Demonstrates targeted backdoor data-poisoning attacks against genomic foundation models trained on DNA sequences. OpenAlex concept tags include "genome, artificial intelligence, sliding window protocol, data mining '.

c19: Public author profile is associated with Shanghai University. Recent work in computer vision includes 'OmniRoam: World Wandering via Long-Horizon Panoramic Video Generation'. Generates long-horizon, navigable panoramic video to model persistent, explorable 3D scenes. OpenAlex concept tags include "biology, computer science, artificial intelligence, gene '.

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c00：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

criteria: 没有具体关联 只有泛泛的领域相似 有具体共同方向或可用的互补能力 有非常明确的共同工作内容或直接互补

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c01：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c02：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c03：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c04：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c05：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c06：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c07：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c08：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c09：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c10：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c11：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c12：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c13：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c14：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c15：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c16：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c17：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c18：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c19：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补


source:
[person] (@[person]). base in Berkeley.

🏍 🛣 🌎 I miss _why.

Representative projects and topics: Give your JS App some Backbone with Models, Views, Collections, and Events; JavaScript's utility _ belt; Unfancy JavaScript; Literate Programming can be Quick and Dirty.; Code as Art, Art as Code. Processing and Ruby are meant for each other.; A TextMate Bundle for CoffeeScript.

{"message":"Moved Permanently","url":"

c00:
[person] (@[person]). base in Stanford.

I like to train Deep Neural Nets on large datasets.

Representative projects and topics: The simplest, fastest repository for training/finetuning medium-sized GPTs.; The best ChatGPT that $100 can buy.; LLM training in simple, raw C/CUDA; Inference Llama 2 in one file of pure C; A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API; AI agents running research on single-GPU nanochat training automatically.

I like deep neural nets.

c01:
[person] (@[person])

Senior Data Scientist

Representative projects and topics: Python utilities to download and parse the MNIST dataset; Service to preview documentation from GitHub artifacts; Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more; A high-performance library for compressed ndarrays, with a flexible computational engine; Tests to read Iceberg directly from pandas; Meson PEP 517 Python build backend.

c02:
[person] (@[person]). works with Dagster Labs.

@dagster-io, @illinois

Representative projects and topics: An orchestration platform for the development, production, and observation of data assets.; july; An encrypted messenger application for Android devices; Sling is a CLI tool that extracts data from a source storage/database and loads it in a target storage/database.; Get started with dbt in less than 1 minute from `git clone` to `dbt docs serve` for free!; The Python Package Index.

c03:
[person] (@[person]). base in Amsterdam.

Software Engineer

Representative projects and topics: Template for DuckDB extensions to help you develop, test and deploy a custom extension; DuckDB is an in-process SQL OLAP Database Management System; duckdb-go provides a database/sql driver for the DuckDB database engine.; for testing purposes; intended for testing conflicting extension loads / functions; DuckLake is an integrated data lake and catalog format.

Charlotte (Lotte) Felius

Publications

G-ALP: Rethinking Light-weight Encodings for GPUs at DaMoN @ SIGMOD 2025
VCrypt: Leveraging Vectorized and Compressed Execution for Client-Side Encryption at EDBT 2025
Enhancing Security for Columnar Storage and Data Lakes at VLDB 2024
DuckDB-SGX2: The Good, The Bad and The Ugly within Confidential Analytical Query Processing at DaMoN @ SIGMOD 2024
Accelerating GPU Data Processing using FastLanes Compression at DaMoN @ SIGMOD 2024

Education
PhD Student at CWI in the Database Architectures Group working on secure query processing
MSc Computational Science (Joint Degree) at University of Amsterdam & Vrije Universiteit Amsterdam
BSc Systems Engineering, Policy Analysis & Management at Delft University of Technology

c05: Public author profile is associated with Anhui University. Recent work in ai and machine learning includes 'Think Anywhere in Code Generation'. Lets a reasoning LLM interleave thinking throughout code generation instead of only reasoning upfront. OpenAlex concept tags include "computer science, programming language, artificial intelligence, software engineering '.

c06: Public author profile is associated with Wenshan University. Recent work in computer vision includes 'Benchmarking PhD-Level Coding in 3D Geometric Computer Vision'. Benchmarks how well AI coding models handle graduate-level 3D geometric computer-vision programming tasks. OpenAlex concept tags include "computer science, medicine, chemistry, artificial intelligence '.

c07: Public author profile is associated with Shihezi University. Recent work in computer vision includes 'Benchmarking PhD-Level Coding in 3D Geometric Computer Vision'. Benchmarks how well AI coding models handle graduate-level 3D geometric computer-vision programming tasks. OpenAlex concept tags include "medicine, biology, internal medicine, oncology '.

c08: Public author profile is associated with Suzhou University of Science and Technology. Recent work in computer vision includes 'Benchmarking PhD-Level Coding in 3D Geometric Computer Vision'. Benchmarks how well AI coding models handle graduate-level 3D geometric computer-vision programming tasks. OpenAlex concept tags include "computer science, artificial intelligence, pattern recognition (psychology), machine learning '.

c09:
[person] (@[person]). works with @bentoml.

emacs otaku

Representative projects and topics: Hua (㕦) is a hy-like lisp language to lua compiler.; [zsy.ds4] is is a simple max patch to handle Dualshock 4 inputs (over bluetooth); retro forth in lua; lightweight script for nanopi oled hat; toy dsp library in python; The Modular Platform (includes MAX & Mojo).

c10: Public author profile is associated with Alibaba Group (United States). Recent work in ai and machine learning includes 'Think Anywhere in Code Generation'. Lets a reasoning LLM interleave thinking throughout code generation instead of only reasoning upfront. OpenAlex concept tags include "computer science, artificial intelligence, physics, programming language '.

c11: Public author profile is associated with Hengyang Normal University. Recent work in computer vision includes 'Benchmarking PhD-Level Coding in 3D Geometric Computer Vision'. Benchmarks how well AI coding models handle graduate-level 3D geometric computer-vision programming tasks. OpenAlex concept tags include "chemistry, medicine, biology, organic chemistry '.

c12: Recent work in computer vision includes 'Benchmarking PhD-Level Coding in 3D Geometric Computer Vision'. Benchmarks how well AI coding models handle graduate-level 3D geometric computer-vision programming tasks. OpenAlex concept tags include "computer science, artificial intelligence, computer vision, machine learning '.

c13: Public author profile is associated with Kunming University of Science and Technology. Recent work in computer vision includes 'Benchmarking PhD-Level Coding in 3D Geometric Computer Vision'. Benchmarks how well AI coding models handle graduate-level 3D geometric computer-vision programming tasks. OpenAlex concept tags include "medicine, chemistry, biology, materials science '.

c14: Public author profile is associated with Princeton University. Recent work in computer vision includes 'Video Models Reason Early: Exploiting Plan Commitment for Maze Solving'. Finds video diffusion models commit to a maze-solving plan early in generation, exploited to speed up reasoning. OpenAlex concept tags include "computer science, artificial intelligence, machine learning, mathematics '.

c15: Public author profile is associated with China Europe International Business School. Recent work in computer vision includes 'Benchmarking PhD-Level Coding in 3D Geometric Computer Vision'. Benchmarks how well AI coding models handle graduate-level 3D geometric computer-vision programming tasks. OpenAlex concept tags include "psychology, social psychology, computer science, political science '.

c16: Public author profile is associated with Guangdong Food and Drug Vocational College. Recent work in computer vision includes 'Benchmarking PhD-Level Coding in 3D Geometric Computer Vision'. Benchmarks how well AI coding models handle graduate-level 3D geometric computer-vision programming tasks. OpenAlex concept tags include "elevator, engineering, operating system, epistemology '.

c17: Public author profile is associated with Peking University. Recent work in ai and machine learning includes 'Think Anywhere in Code Generation'. Lets a reasoning LLM interleave thinking throughout code generation instead of only reasoning upfront. OpenAlex concept tags include "computer science, artificial intelligence, programming language, software engineering '.

c18: Public author profile is associated with University of California, Berkeley. Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. An autonomous agent that plans and runs de novo drug-design iterations, validated against real wet-lab results. OpenAlex concept tags include "biology, computational biology, cancer, gene '.

c19: Public author profile is associated with Xiamen University. Recent work in ai and machine learning includes 'Think Anywhere in Code Generation'. Lets a reasoning LLM interleave thinking throughout code generation instead of only reasoning upfront. OpenAlex concept tags include "chemistry, materials science, condensed matter physics, physics '.

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c00：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

criteria: 没有具体关联 只有泛泛的领域相似 有具体共同方向或可用的互补能力 有非常明确的共同工作内容或直接互补

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c01：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c02：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c03：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c04：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c05：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c06：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c07：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c08：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c09：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c10：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c11：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c12：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c13：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c14：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c15：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c16：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c17：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c18：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c19：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补


source:
[person] (@[person])

Representative projects and topics: Sampling CPU profiler for Ruby; Recipes for using Python's pandas library; spy on the DNS queries your computer is making; Source for Mess With DNS; simple markdown translator; twine.

hello!

The main repositories here are a Ruby profiler I wrote called rbspy and a cookbook for learning pandas. There are also dozens of other tiny toy projects that I made for fun once and don't maintain at all.

Some other places you can find my work on the internet:

blog:
zines:

c00:
[person] (@[person]). base in San Francisco, CA.

Founder/CEO of BentoML, previously @databricks, ML/AI Platforms & Systems, Product Design

Representative projects and topics: The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!; Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud.; Applying Genetic Algorithm to Travelling Salesman Problem; Unified Model Serving Framework; Benchmark and optimize LLM inference across frameworks with ease; Everything you need to know about LLM inference.

Hi there 👋

🔭 I’m currently working on 🍱 BentoML - 🙋 We're hiring!
🤖 Ask me about building ML/AI applications
📷 Photography: Eyes of San Francisco

c01:
[person] (@[person]). base in Singapore; works with VoidZero.

Founder @voidzero-dev, author of @vuejs and @vitejs. Connoisseur of sushi.

Representative projects and topics: 🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.; Next generation frontend tooling. It's fast!; Vite & Vue powered static site generator.; Fast Rust bundler for JavaScript/TypeScript with Rollup-compatible API.; This is the repo for Vue 2. For Vue 3, go to; 6kb subset of Vue optimized for progressive enhancement.

c02:
[person] (@[person]). base in Sacramento; works with Mathematics and Statistics Department, CSU Sacramento.

Statistics Professor @ Sacramento State

Representative projects and topics: Unmaintained scripts; Utility scripts, python modules, and dotfiles to be stored in home folder; Log of research activities for PhD in statistics; Transform Serial R Code into Parallel R Code; course materials for UC Davis STA141C: Big Data & High Performance Statistical Computing; running examples on slurm cluster.

c03:
[person] (@[person]). base in Bristol, UK; works with University of Bristol.

Climate, glaciers, and programming.

Representative projects and topics: Open Global Glacier Model; Add geolocalised subsetting, masking, and plotting operations to xarray; N-D labeled arrays and datasets in Python; Lectures notes of the scientific programming course at the University of Innsbruck; Educational material about glaciers, powered by OGGM and MyBinder; Class activities and notebooks for the MSc "Quantifying Climate Risks" class taught at Uni Bristol.

c04:
[person] (@[person]). base in Amsterdam.

Software Engineer

Representative projects and topics: Template for DuckDB extensions to help you develop, test and deploy a custom extension; DuckDB is an in-process SQL OLAP Database Management System; duckdb-go provides a database/sql driver for the DuckDB database engine.; for testing purposes; intended for testing conflicting extension loads / functions; DuckLake is an integrated data lake and catalog format.

Charlotte (Lotte) Felius

Publications

G-ALP: Rethinking Light-weight Encodings for GPUs at DaMoN @ SIGMOD 2025
VCrypt: Leveraging Vectorized and Compressed Execution for Client-Side Encryption at EDBT 2025
Enhancing Security for Columnar Storage and Data Lakes at VLDB 2024
DuckDB-SGX2: The Good, The Bad and The Ugly within Confidential Analytical Query Processing at DaMoN @ SIGMOD 2024
Accelerating GPU Data Processing using FastLanes Compression at DaMoN @ SIGMOD 2024

Education
PhD Student at CWI in the Database Architectures Group working on secure query processing
MSc Computational Science (Joint Degree) at University of Amsterdam & Vrije Universiteit Amsterdam
BSc Systems Engineering, Policy Analysis & Management at Delft University of Technology

c05:
[person] (@[person]). base in Amsterdam; works with DuckDB Labs.

Mostly DuckDB

Representative projects and topics: DuckDB is an analytical in-process SQL database management system; New Parser for DuckDB; DuckDB is an embeddable SQL OLAP Database Management System; Library to read a subset of Parquet files; duckdb extension for curl; Website [person].

c06: Public author profile is associated with Suzhou University of Science and Technology. Recent work in computer vision includes 'Benchmarking PhD-Level Coding in 3D Geometric Computer Vision'. Benchmarks how well AI coding models handle graduate-level 3D geometric computer-vision programming tasks. OpenAlex concept tags include "computer science, artificial intelligence, pattern recognition (psychology), machine learning '.

c07: Public author profile is associated with Princeton University. Recent work in computer vision includes 'Video Models Reason Early: Exploiting Plan Commitment for Maze Solving'. Finds video diffusion models commit to a maze-solving plan early in generation, exploited to speed up reasoning. OpenAlex concept tags include "computer science, artificial intelligence, machine learning, mathematics '.

c08: Public author profile is associated with Council of Agriculture. Recent work in ai and machine learning includes 'Reward-Based Online LLM Routing via NeuralUCB'. Uses a NeuralUCB bandit algorithm to route queries among LLMs online for cost-aware reward optimization. OpenAlex concept tags include "irrigation, water resource management, environmental science, engineering '.

c09: Public author profile is associated with Google DeepMind (United Kingdom). Recent work in ai and machine learning includes 'Refined Detection for Gumbel Watermarking'. Proposes a near-optimal detection method for Aaronson's Gumbel scheme for watermarking LLM output. OpenAlex concept tags include "computer science, mathematics, mathematical optimization, artificial intelligence '.

c10: YC batch S2020 founder at Humanloop. Role: Founder. Machine Learning and Engineering at Cambridge and MIT.

c11: Public author profile is associated with University of Padua. Recent work in computational biology includes 'Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction'. Finds that sampling the loss landscape at intermediate Langevin-dynamics temperatures best trains transformers for protein structure prediction. OpenAlex concept tags include "microstructure, materials science, metallurgy, chemistry '.

c12: YC batch W2024 founder at Sonauto. Role: Founder. Previously a software engineering intern at Vocode (YC W23) and an intern on the Machine Learning Team at Ada.

c13: Public author profile is associated with Texas Tech University. Recent work in ai and machine learning includes 'Reward-Based Online LLM Routing via NeuralUCB'. Uses a NeuralUCB bandit algorithm to route queries among LLMs online for cost-aware reward optimization. OpenAlex concept tags include "biology, medicine, chemistry, bacteria '.

c14: YC batch S2023 founder at Automorphic. Role: Founder. Building AI agents that can remember and self-improve at Automorphic.

c15: YC batch W2023 founder at Trigger.dev. Role: Co-founder. Co-founder of Trigger.dev; the open source background jobs platform, and JSON Hero.

c17: Recent work in computational biology includes 'Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction'. Finds that sampling the loss landscape at intermediate Langevin-dynamics temperatures best trains transformers for protein structure prediction. OpenAlex concept tags include "computational biology, computer science, biology '.

c18: YC batch W2023 founder at Trigger.dev. Role: CEO. CEO of Trigger.dev. Previously created two iPad apps that won Apple's iPad app of the year award. Worked on several startups including building JSON Hero.

c19: Public author profile is associated with China Europe International Business School. Recent work in computer vision includes 'Benchmarking PhD-Level Coding in 3D Geometric Computer Vision'. Benchmarks how well AI coding models handle graduate-level 3D geometric computer-vision programming tasks. OpenAlex concept tags include "psychology, social psychology, computer science, political science '.

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c00：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

criteria: 没有具体关联 只有泛泛的领域相似 有具体共同方向或可用的互补能力 有非常明确的共同工作内容或直接互补

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c01：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c02：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c03：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c04：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c05：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c06：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c07：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c08：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c09：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c10：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c11：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c12：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c13：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c14：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c15：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c16：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c17：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c18：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c19：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补


source:
[person] (@[person]). base in Stanford.

I like to train Deep Neural Nets on large datasets.

Representative projects and topics: The simplest, fastest repository for training/finetuning medium-sized GPTs.; The best ChatGPT that $100 can buy.; LLM training in simple, raw C/CUDA; Inference Llama 2 in one file of pure C; A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API; AI agents running research on single-GPU nanochat training automatically.

I like deep neural nets.

c00:
[person] (@[person]). base in Zurich, Switzerland; works with Microsoft.

I am a software engineer at Microsoft in Zurich, Switzerland since 2011. Our team started VS Code when it was still called Monaco.

Representative projects and topics: Visual Studio Code; :electron: Build cross-platform desktop apps with JavaScript, HTML, and CSS; The target platform to use to build RSSOwl 2.x; A page to show unhandled Copilot PR review comments; Yo code test extension; Run a command using sudo, prompting the user with an OS dialog if necessary..

{"message":"Moved Permanently","url":"

c03:
[person] (@[person]). base in Berkeley.

🏍 🛣 🌎 I miss _why.

Representative projects and topics: Give your JS App some Backbone with Models, Views, Collections, and Events; JavaScript's utility _ belt; Unfancy JavaScript; Literate Programming can be Quick and Dirty.; Code as Art, Art as Code. Processing and Ruby are meant for each other.; A TextMate Bundle for CoffeeScript.

{"message":"Moved Permanently","url":"

c04: Public author profile is associated with Indian Institute of Science Education and Research Kolkata. Recent work in computational materials and physics includes 'Learning the Exact Flux: Neural Riemann Solvers with Hard Constraints'. Trains a neural network to approximate exact Riemann-problem fluxes for Godunov-type computational fluid dynamics solvers. OpenAlex concept tags include "computer science, materials science, artificial intelligence, physics '.

c05: Public author profile is associated with Technological University Dublin. Recent work in ai and machine learning includes 'Meteorology-Driven GPT4AP: A Multi-Task Forecasting LLM for Atmospheric Air Pollution in Data-Scarce Settings'. A multi-task LLM that uses meteorological inputs to forecast air pollution in regions with sparse monitoring data. OpenAlex concept tags include "computer science, artificial intelligence, machine learning, engineering '.

c06: Public author profile is associated with Council of Agriculture. Recent work in ai and machine learning includes 'Reward-Based Online LLM Routing via NeuralUCB'. Uses a NeuralUCB bandit algorithm to route queries among LLMs online for cost-aware reward optimization. OpenAlex concept tags include "irrigation, water resource management, environmental science, engineering '.

c07: Recent work in ai and machine learning includes 'Tracking Equivalent Mechanistic Interpretations Across Neural Networks'. Studies when different mechanistic-interpretability explanations of separately trained neural networks are functionally equivalent. OpenAlex concept tags include "modular design, computer science, artificial intelligence, psychology '.

c08: Recent work in ai and machine learning includes 'Tracking Equivalent Mechanistic Interpretations Across Neural Networks'. Studies when different mechanistic-interpretability explanations of separately trained neural networks are functionally equivalent. OpenAlex concept tags include "computer science, artificial intelligence, psychology, natural language processing '.

c09: Public author profile is associated with Technological University Dublin. Recent work in ai and machine learning includes 'Meteorology-Driven GPT4AP: A Multi-Task Forecasting LLM for Atmospheric Air Pollution in Data-Scarce Settings'. A multi-task LLM that uses meteorological inputs to forecast air pollution in regions with sparse monitoring data. OpenAlex concept tags include "computer science, artificial intelligence, geography, engineering '.

c10:
[person] (@[person]). base in Amsterdam.

Software Engineer

Representative projects and topics: Template for DuckDB extensions to help you develop, test and deploy a custom extension; DuckDB is an in-process SQL OLAP Database Management System; duckdb-go provides a database/sql driver for the DuckDB database engine.; for testing purposes; intended for testing conflicting extension loads / functions; DuckLake is an integrated data lake and catalog format.

Charlotte (Lotte) Felius

Publications

G-ALP: Rethinking Light-weight Encodings for GPUs at DaMoN @ SIGMOD 2025
VCrypt: Leveraging Vectorized and Compressed Execution for Client-Side Encryption at EDBT 2025
Enhancing Security for Columnar Storage and Data Lakes at VLDB 2024
DuckDB-SGX2: The Good, The Bad and The Ugly within Confidential Analytical Query Processing at DaMoN @ SIGMOD 2024
Accelerating GPU Data Processing using FastLanes Compression at DaMoN @ SIGMOD 2024

Education
PhD Student at CWI in the Database Architectures Group working on secure query processing
MSc Computational Science (Joint Degree) at University of Amsterdam & Vrije Universiteit Amsterdam
BSc Systems Engineering, Policy Analysis & Management at Delft University of Technology

c11:
[person] (@[person]). works with Prefect.

Platform Engineer @PrefectHQ

Representative projects and topics: Configs for my development environment.; Resume in Markdown, styled with CSS, output to HTML and PDF.

c12: YC batch S2025 founder at Modelence. Role: Co-Founder & CEO. Before Modelence, I co-founded CodeSignal (valued $500M) and spent a decade on building and scaling one of the largest skill assessment platforms, used by millions of users. Forbes 30u30 US & Canada, Enterprise Technology. Silver medalist of IOI 2007.

c13: Public author profile is associated with Wuhan University of Technology. Recent work in computational materials and physics includes 'Learning the Exact Flux: Neural Riemann Solvers with Hard Constraints'. Trains a neural network to approximate exact Riemann-problem fluxes for Godunov-type computational fluid dynamics solvers. OpenAlex concept tags include "psychology, computer science, social psychology, business '.

c14: Public author profile is associated with Texas Tech University. Recent work in ai and machine learning includes 'Reward-Based Online LLM Routing via NeuralUCB'. Uses a NeuralUCB bandit algorithm to route queries among LLMs online for cost-aware reward optimization. OpenAlex concept tags include "biology, medicine, chemistry, bacteria '.

c15: Public author profile is associated with University College Dublin. Recent work in ai and machine learning includes 'Meteorology-Driven GPT4AP: A Multi-Task Forecasting LLM for Atmospheric Air Pollution in Data-Scarce Settings'. A multi-task LLM that uses meteorological inputs to forecast air pollution in regions with sparse monitoring data. OpenAlex concept tags include "computer science, artificial intelligence, environmental science, meteorology '.

c16: YC batch W2026 founder at CellType. Role: Co-Founder. Co-Founder @ CellType (W26). ML @ Yale & EPFL. Co-developed Cell2Sentence at Yale and published at ICML. Previously led large-scale foundation model training at a biotech startup and built software to control CERN's Large Hadron Collider.

c18: Public author profile is associated with University of Padua. Recent work in computational biology includes 'Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction'. Finds that sampling the loss landscape at intermediate Langevin-dynamics temperatures best trains transformers for protein structure prediction. OpenAlex concept tags include "microstructure, materials science, metallurgy, chemistry '.

c19: Public author profile is associated with Google DeepMind (United Kingdom). Recent work in ai and machine learning includes 'Refined Detection for Gumbel Watermarking'. Proposes a near-optimal detection method for Aaronson's Gumbel scheme for watermarking LLM output. OpenAlex concept tags include "computer science, mathematics, mathematical optimization, artificial intelligence '.

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c00：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

criteria: 没有具体关联 只有泛泛的领域相似 有具体共同方向或可用的互补能力 有非常明确的共同工作内容或直接互补

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c01：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c02：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c03：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c04：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c05：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c06：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c07：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c08：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c09：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c10：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c11：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c12：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c13：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c14：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c15：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c16：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c17：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c18：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

type: score

instructions: 只比较 on.source 与 ctx[0].candidates.c19：双方职业、研究、技能或资源存在多强的具体关联，值得进一步交流或协作？依据这两份材料中的实际内容，区分泛泛相似与具体共同方向、互补能力；不得编造经历、意愿或已发生的合作。同批其他候选是独立问题，不作为这个候选的证据。

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补

有具体共同方向或可用的互补能力

有非常明确的共同工作内容或直接互补