source: Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. Drug discovery relies on iterative expert workflows that are slow to parallelize and difficult to scale... OpenAlex concept tags include "mathematics, computer science, artificial intelligence, machine learning '.

c00:
[person] (@[person]). works with @langchain-ai.

Trace tackler @ LangChain

Representative projects and topics: An Open-Source Asynchronous Coding Agent; 📃 A better UX for chat, writing content, and coding with LLMs.; An open-source, no-code agent building platform.; 📲 An agent for sourcing, curating, and scheduling social media posts with human-in-the-loop.; 🦜💬 Web app for interacting with any LangGraph agent (PY & TS) via a chat interface.; 📥 An inbox UX for interacting with human-in-the-loop agents..

[](

c01:
[person] (@[person]). base in Cambridge, MA.

Engineering @langchain-ai

Representative projects and topics: The agent engineering platform; Build resilient language agents as graphs.; Agent harness built with LangChain and LangGraph. Equipped with a planning tool, a filesystem backend, and the ability to spawn subagents - well-equipped to handle complex agentic tasks.; 🦜🔗 Docs for LangChain projects; Webhook trigger option for LangSmith's Fleet; ID vinyl records with Computer Vision & fetch metadata for radio DJs.

c02:
[person] (@[person]). base in Austin, TX; works with NVIDIA.

Representative projects and topics: Multi-agent project starter for Claude Code. Fill in PROJECT.md, run /bootstrap, start building.; Experiment in AI Agent tools via creation of an email assistant.; cuTile is a programming model for writing parallel kernels for NVIDIA GPUs; NVIDIA curated collection of educational resources related to general purpose GPU programming.; Python for HPC Tutorial Notebooks; The NumFOCUS DISCOVER Cookbook (Diverse & Inclusive Spaces and Conferences: Overall Vision and Essential Resources). A guide for organizing more diverse and inclusive events and conferences, produced by the NumFOCUS Diversity & Inclusion in Scientific Computing (DISC) Program, with support from the Moore Foundation..

Hi there 👋

🔭 I’m currently working on CUDA Python @ NVIDIA
👯 I’m looking to collaborate on DISCOVER Cookbook, gpu dsls
😄 Pronouns: he / him

📊 GitHub Stats:

c03: Public author profile is associated with Schott (Germany). Recent work in computational materials and physics includes 'Molecular beam epitaxy of wafer-scale O-band InAs/InGaAs quantum dots on GaAs for quantum photonics'. We report a scalable molecular beam epitaxy strategy to achieve a low density of O-band electrically tunable InAs/InGaAs quantum dots (QDs) on GaAs(001) substrates... OpenAlex concept tags include "physics, quantum dot, photon, optoelectronics '.

c04:
[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.

c05: Public author profile is associated with Erasmus MC. Recent work in computer vision includes 'SurgNavAR: An Augmented Reality Surgical Navigation Framework for Optical See-Through Head Mounted Displays'. Augmented reality (AR) devices with head mounted displays (HMDs) facilitate the direct superimposition of 3D preoperative imaging data onto the patient during surgery... OpenAlex concept tags include "neurocognitive, cognition, psychiatry, psychology '.

c06: 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.

c07: 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.

c08: Public author profile is associated with Schott (Germany). Recent work in computational materials and physics includes 'Molecular beam epitaxy of wafer-scale O-band InAs/InGaAs quantum dots on GaAs for quantum photonics'. We report a scalable molecular beam epitaxy strategy to achieve a low density of O-band electrically tunable InAs/InGaAs quantum dots (QDs) on GaAs(001) substrates... OpenAlex concept tags include "quantum dot, photon, optoelectronics, physics '.

c09:
[person] (@[person]). base in London, United Kingdom; works with @pydantic.

The Pydantic Stack: Pydantic Validation, Pydantic AI and Pydantic Logfire

Representative projects and topics: Data validation using Python type hints; AI Agent Framework, the Pydantic way; A minimal, secure Python interpreter written in Rust for use by AI; AI observability platform for production LLM and agent systems.; Simple, modern and fast file watching and code reload for Python, written in Rust; Dev tools for python.

c10: Public author profile is associated with Erasmus MC. Recent work in computer vision includes 'SurgNavAR: An Augmented Reality Surgical Navigation Framework for Optical See-Through Head Mounted Displays'. Augmented reality (AR) devices with head mounted displays (HMDs) facilitate the direct superimposition of 3D preoperative imaging data onto the patient during surgery... OpenAlex concept tags include "medicine, surgery, radiology, internal medicine '.

c11: Public author profile is associated with University of Würzburg. Recent work in computational materials and physics includes 'Molecular beam epitaxy of wafer-scale O-band InAs/InGaAs quantum dots on GaAs for quantum photonics'. We report a scalable molecular beam epitaxy strategy to achieve a low density of O-band electrically tunable InAs/InGaAs quantum dots (QDs) on GaAs(001) substrates... OpenAlex concept tags include "quantum dot, physics, photon, quantum '.

c12: Public author profile is associated with University of Bari Aldo Moro. Recent work in quantitative social science and economics includes 'Violence Against Women: a pilot study on the perception of Apulian High school students'. Violence Against Women (VAW) is a widespread issue deeply rooted in social and cultural structures... OpenAlex concept tags include "biology, statistics, geography, ecology '.

c13:
[person] (@[person]). base in Chicago; works with Canonical.

open science enthusiast ~ software developer ~ an under the sea code monkey

Representative projects and topics: The snapd and snap tools enable systems to work with .snap files.; PMR ( configs used by CheckBox developers; Creating delicious APIs for Django apps since 2010. (fork); toy app to demonstrate some tastypie behavior; The official website for the Ubuntu operating system; A tool to create Ubuntu images for embedded systems - mainly ARM/ARM64 boards like Beaglebone, RaspberryPi 2, RaspberryPI 3 or 96Boards DragonBoard410c.

c14: 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.

c15:
[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.

c17: 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.

c18: YC batch W2024 founder at Sonauto. Role: Founder. Studied Computer Science (focus: ML) and History at Cornell. Previously worked on core engineering teams at early and growth stage AI and creator economy startups.

c19:
[person] (@[person]). base in Amsterdam; works with @pydantic.

[contact removed]

Representative projects and topics: Data validation using Python type hints; A flake8 plugin to check Pydantic related code.; Automatically add type hints for Django powered applications.; An extensible and sane implementation of JsonLogic; A Python library for tree data structures with an intuitive, yet powerful API.; PyPy is a very fast and compliant implementation of the Python language..

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: Public author profile is associated with Max Planck Institute for Dynamics of Complex Technical Systems. Recent work in ai and machine learning includes 'Tucker Attention: A generalization of approximate attention mechanisms'. The pursuit of reducing the memory footprint of the self-attention mechanism in multi-headed self attention (MHA) spawned a rich portfolio of methods, e.g., group-query attention (GQA) and multi-head latent attention (MLA)... OpenAlex concept tags include "mathematics, computer science, mathematical optimization, artificial intelligence '.

c00:
[person] (@[person])

Full-Time Open-Sourcerer. Focused on Swift & JavaScript. Makes macOS apps, CLI tools, npm packages.

Representative projects and topics: A collection of essential TypeScript types; 🌳 Tiny & elegant JavaScript HTTP client based on the Fetch API; 😎 Awesome lists about all kinds of interesting topics; 🤖 Awesome list for ChatGPT — an artificial intelligence chatbot developed by OpenAI; ⌨ Add user-customizable global keyboard shortcuts (hotkeys) to your macOS app in minutes; 💾 Swifty and modern UserDefaults.

i love code** **and unicorns

check out my latest app: Supercharge

and my latest blog post

c01:
[person] (@[person]). base in Austin, TX; works with NVIDIA.

Representative projects and topics: Multi-agent project starter for Claude Code. Fill in PROJECT.md, run /bootstrap, start building.; Experiment in AI Agent tools via creation of an email assistant.; cuTile is a programming model for writing parallel kernels for NVIDIA GPUs; NVIDIA curated collection of educational resources related to general purpose GPU programming.; Python for HPC Tutorial Notebooks; The NumFOCUS DISCOVER Cookbook (Diverse & Inclusive Spaces and Conferences: Overall Vision and Essential Resources). A guide for organizing more diverse and inclusive events and conferences, produced by the NumFOCUS Diversity & Inclusion in Scientific Computing (DISC) Program, with support from the Moore Foundation..

Hi there 👋

🔭 I’m currently working on CUDA Python @ NVIDIA
👯 I’m looking to collaborate on DISCOVER Cookbook, gpu dsls
😄 Pronouns: he / him

📊 GitHub Stats:

c02: Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. Drug discovery relies on iterative expert workflows that are slow to parallelize and difficult to scale...

c03: Recent work in quantitative social science and economics includes 'Longest weakly increasing subsequences of discrete random walks on the integers with heavy tailed distribution of increments'. We investigate the behavior of the length of the longest weakly increasing subsequences (weak LIS) of $n$-step random walks with nonzero integer increments $k = \pm 1, \pm 2, \dots$ given by a zero-mean, symmetric heavy tailed mass distribution proportional to $|k|^{-1-α}$ for several values of the real parameter $α> 0$ together with that of the simple random walk ($k=\pm 1$), to which the $n$-step heavy tailed random walks tends when $α> (1+o(1))\log_{2}{n}$... OpenAlex concept tags include "poisson distribution, statistics, statistical physics, random variable '.

c04:
[person] (@[person]). base in Half Moon Bay, California; works with Datasette.

Representative projects and topics: An open source multi-tool for exploring and publishing data; Python CLI utility and library for manipulating SQLite databases; Access large language models from the command-line; A command-line utility for taking automated screenshots of websites; Concatenate a directory full of files into a single prompt for use with LLMs; A tool for creating credentials for accessing S3 buckets.

c05: YC batch S2020 founder at Humanloop. Role: Founder. Machine Learning and Engineering at Cambridge and MIT.

c06:
[person] (@[person]). base in Schaumburg, IL; works with @PrefectHQ.

Software Engineer @PrefectHQ

Representative projects and topics: Prefect is a workflow orchestration framework for building resilient data pipelines in Python.; An experiment in distributed memoization; MCP server for delegating tasks to Devin AI; Generate MDX API documentation from Python modules; Test project for `include_files` feature with the `prefect-aws` `@ecs` decorator; 🫏 Add a stubborn streak to your code..

c07:
[person] (@[person]). base in Mountain View, California; works with Google.

Engineering Lead at Google working on Chrome & Web Platform

Representative projects and topics: ⚡Faster subsequent page-loads by prefetching in-viewport links during idle time; 📦 Workbox: JavaScript libraries for Progressive Web Apps; Extract & Inline Critical-path CSS in HTML pages; Material Design Components in HTML/CSS/JS; Helping you select a JavaScript framework - Todo apps for React.js, Angular, Vue and many more; Yeoman - a set of tools for automating development workflow.

🔭 I’m currently working on AI at Google (Gemini, Agents, Vertex)
👯 I’m looking to collaborate on Developer Tooling
🤔 I’m looking for help with Framework collaborations
💬 Ask me about anything

c08:
[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":"

c09:
[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.

c10:
[person] (@[person]). base in Austria; works with Earendil.

Software developer and Open Source nut. Creator of the Flask framework. Founder of @earendil-works. Other things of interest: @pallets and @getsentry

Representative projects and topics: The Python micro framework for building web applications.; MiniJinja is a powerful but minimal dependency template engine for Rust compatible with Jinja/Jinja2; A snapshot testing library for rust; A very fast and expressive template engine.; Python composable command line interface toolkit; These are commands I use with agents, mostly Claude.

c11:
[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

c12:
[person] (@[person])

(hello) => there

Representative projects and topics: A repo for getting started with Prefect.; Production repository for the all-new Advantage360 Professional using ZMK engine; Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms, Tasks, Search & Drive with AI - Comprehensive Google Workspace / G Suite MCP Server; Excel-like grid component built with React, with editors, keyboard navigation, copy & paste, and the like; A MCP server for the Frankfurter API for currency exchange rates..

c13:
[person] (@[person]). base in San Francisco; works with @langchain-ai.

Building agent frameworks @langchain-ai. Head of the @webdriverio project. Open source & open standards advocate.

Representative projects and topics: Next-gen browser and mobile automation test framework for Node.js; A curated list of delightful Selenium resources.; Boilerplate project to run WebdriverIO tests with Cucumber; a lightweight rgb/rgba to hex parser; A Node.js based package and CLI library for launching Selenium with WebDrivers support (Chrome, Firefox, IE, Edge); Boilerplate project to run WebdriverIO tests with Appium to test native applications on iOS and Android.

Open Source & Open Standards Advocate.

🔭 I’m currently working at **LangChain** building Agent Frameworks

🤝 I’m automating things with **WebdriverIO

📝 I regulary write articles in **my blog

🧠 I'm learning Video Editing with Final Cut Pro

💬 Ask me about **WebdriverIO / Test Automation / VSCode Extension Development / Node.js / TypeScript

⚡ Fun fact **I grew up on the country side in Germany next to 🐮 and 🐔

Skills

c14:
[person] (@[person]). base in Chicago; works with Canonical.

open science enthusiast ~ software developer ~ an under the sea code monkey

Representative projects and topics: The snapd and snap tools enable systems to work with .snap files.; PMR ( configs used by CheckBox developers; Creating delicious APIs for Django apps since 2010. (fork); toy app to demonstrate some tastypie behavior; The official website for the Ubuntu operating system; A tool to create Ubuntu images for embedded systems - mainly ARM/ARM64 boards like Beaglebone, RaspberryPi 2, RaspberryPI 3 or 96Boards DragonBoard410c.

c15: YC batch W2024 founder at Sonauto. Role: Founder. Studied Computer Science (focus: ML) and History at Cornell. Previously worked on core engineering teams at early and growth stage AI and creator economy startups.

c16:
[person] (@[person]). base in London, United Kingdom; works with @pydantic.

The Pydantic Stack: Pydantic Validation, Pydantic AI and Pydantic Logfire

Representative projects and topics: Data validation using Python type hints; AI Agent Framework, the Pydantic way; A minimal, secure Python interpreter written in Rust for use by AI; AI observability platform for production LLM and agent systems.; Simple, modern and fast file watching and code reload for Python, written in Rust; Dev tools for python.

c17: Recent work in computer vision includes 'OmniRoam: World Wandering via Long-Horizon Panoramic Video Generation'. Modeling scenes using video generation models has garnered growing research interest in recent years... OpenAlex concept tags include "medicine, biology, internal medicine, pathology '.

c18: Recent work in computational biology includes 'A Hierarchical Sheaf Spectral Embedding Framework for Single-Cell RNA-seq Analysis'. Single-cell RNA-seq data analysis typically requires representations that capture heterogeneous local structure across multiple scales while remaining stable and interpretable... OpenAlex concept tags include "betti number, physics, combinatorics, algorithm '.

c19: Recent work in computational materials and physics includes 'Molecular beam epitaxy of wafer-scale O-band InAs/InGaAs quantum dots on GaAs for quantum photonics'. We report a scalable molecular beam epitaxy strategy to achieve a low density of O-band electrically tunable InAs/InGaAs quantum dots (QDs) on GaAs(001) substrates... OpenAlex concept tags include "optics, physics, electron, engineering '.

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: Public author profile is associated with Anhui Polytechnic University. Recent work in computational materials and physics includes 'Learning the Exact Flux: Neural Riemann Solvers with Hard Constraints'. Godunov-type methods, which obtain numerical fluxes through local Riemann problems at cell interfaces, are among the most fundamental and widely used numerical methods in computational fluid dynamics... OpenAlex concept tags include "china, economics, business, political science '.

c00:
[person] (@[person]). base in Santa Clara, CA; works with @BentoML.

CNCF Ambassador & KubeVela Maintainer (,,• ₃ •,,)

Representative projects and topics: A self-evolving personal AI assistant.; The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!; Declarative Workflow of KubeVela which can run as standalone.; A cloud-native Pipeline resource.; The Modern Application Platform.; Docker for Your ML/DL Models Based on OCI Artifacts.

[person]

I'm currently working on KubeVela & BentoML :sparkles:.

c01:
[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

c02: 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.

c03: Recent work in ai and machine learning includes 'Aligned, Orthogonal or In-conflict: When can we safely optimize Chain-of-Thought?'. Chain-of-Thought (CoT) monitoring, in which automated systems monitor the CoT of an LLM, is a promising approach for effectively overseeing AI systems...

c04: YC batch S2020 founder at Humanloop. Role: Founder. Machine Learning and Engineering at Cambridge and MIT.

c05:
[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.

c06:
[person] (@[person]). base in Chicago; works with Canonical.

open science enthusiast ~ software developer ~ an under the sea code monkey

Representative projects and topics: The snapd and snap tools enable systems to work with .snap files.; PMR ( configs used by CheckBox developers; Creating delicious APIs for Django apps since 2010. (fork); toy app to demonstrate some tastypie behavior; The official website for the Ubuntu operating system; A tool to create Ubuntu images for embedded systems - mainly ARM/ARM64 boards like Beaglebone, RaspberryPi 2, RaspberryPI 3 or 96Boards DragonBoard410c.

c07: Recent work in computer vision includes 'OmniRoam: World Wandering via Long-Horizon Panoramic Video Generation'. Modeling scenes using video generation models has garnered growing research interest in recent years... OpenAlex concept tags include "computer science, artificial intelligence, computer vision, computer graphics (images) '.

c08: Recent work in ai and machine learning includes 'Think Anywhere in Code Generation'. Recent advances in reasoning Large Language Models (LLMs) have primarily relied on upfront thinking, where reasoning occurs before final answer... OpenAlex concept tags include "artificial intelligence, generative model, algorithm, algebra over a field '.

c09:
[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

c10:
[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.

c11:
[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.

c12:
[person] (@[person]). base in Schaumburg, IL; works with @PrefectHQ.

Software Engineer @PrefectHQ

Representative projects and topics: Prefect is a workflow orchestration framework for building resilient data pipelines in Python.; An experiment in distributed memoization; MCP server for delegating tasks to Devin AI; Generate MDX API documentation from Python modules; Test project for `include_files` feature with the `prefect-aws` `@ecs` decorator; 🫏 Add a stubborn streak to your code..

c14: YC batch W2023 founder at Nango. Role: Founder. Founded Nango after seeing first hand how hard it is to build product integrations between SaaS. Previously co-founded Avrios, a B2B SaaS for car fleet management, and grew it to 1000+ customers.

c15: YC batch W2024 founder at Ecliptor. Role: Founder. EECS from the University of Michigan, concentrated in machine learning + system and product design. Before founding Ecliptor, ML engineer on the Recommendations team at Twitch, and prior internships in product @ Nutanix and IoT Eng @ GE.

c16:
[person] (@[person]). base in Austin, TX; works with NVIDIA.

Representative projects and topics: Multi-agent project starter for Claude Code. Fill in PROJECT.md, run /bootstrap, start building.; Experiment in AI Agent tools via creation of an email assistant.; cuTile is a programming model for writing parallel kernels for NVIDIA GPUs; NVIDIA curated collection of educational resources related to general purpose GPU programming.; Python for HPC Tutorial Notebooks; The NumFOCUS DISCOVER Cookbook (Diverse & Inclusive Spaces and Conferences: Overall Vision and Essential Resources). A guide for organizing more diverse and inclusive events and conferences, produced by the NumFOCUS Diversity & Inclusion in Scientific Computing (DISC) Program, with support from the Moore Foundation..

Hi there 👋

🔭 I’m currently working on CUDA Python @ NVIDIA
👯 I’m looking to collaborate on DISCOVER Cookbook, gpu dsls
😄 Pronouns: he / him

📊 GitHub Stats:

c18: 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.

c19: YC batch W2024 founder at Sonauto. Role: Founder. Studied Computer Science (focus: ML) and History at Cornell. Previously worked on core engineering teams at early and growth stage AI and creator economy startups.

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: Public author profile is associated with Sun Yat-sen University. Recent work in quantitative social science and economics includes 'A Bayesian Finite Mixture Model Approach for Mixed-type Data Clustering and Variable Selection with Censored Biomarkers'. Clustering mixed-type data remains a major challenge in biomedical research to uncover clinically meaningful subgroups within heterogeneous patient populations... OpenAlex concept tags include "chemistry, materials science, biology, biochemistry '.

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: YC batch S2023 founder at Parea. Role: Founder. Published first paper in the highest-ranked number theory journal at age 17. M.Sc. from NYU as a Fulbright scholar. Researched applications of deep learning to medical imaging at Covera Health. Built semantic search systems and automatic code generation software at Jina AI.

c02:
[person] (@[person]). base in Chicago; works with Canonical.

open science enthusiast ~ software developer ~ an under the sea code monkey

Representative projects and topics: The snapd and snap tools enable systems to work with .snap files.; PMR ( configs used by CheckBox developers; Creating delicious APIs for Django apps since 2010. (fork); toy app to demonstrate some tastypie behavior; The official website for the Ubuntu operating system; A tool to create Ubuntu images for embedded systems - mainly ARM/ARM64 boards like Beaglebone, RaspberryPi 2, RaspberryPI 3 or 96Boards DragonBoard410c.

c03:
[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.

c04:
[person] (@[person]). base in San Francisco; works with @langchain-ai.

Building agent frameworks @langchain-ai. Head of the @webdriverio project. Open source & open standards advocate.

Representative projects and topics: Next-gen browser and mobile automation test framework for Node.js; A curated list of delightful Selenium resources.; Boilerplate project to run WebdriverIO tests with Cucumber; a lightweight rgb/rgba to hex parser; A Node.js based package and CLI library for launching Selenium with WebDrivers support (Chrome, Firefox, IE, Edge); Boilerplate project to run WebdriverIO tests with Appium to test native applications on iOS and Android.

Open Source & Open Standards Advocate.

🔭 I’m currently working at **LangChain** building Agent Frameworks

🤝 I’m automating things with **WebdriverIO

📝 I regulary write articles in **my blog

🧠 I'm learning Video Editing with Final Cut Pro

💬 Ask me about **WebdriverIO / Test Automation / VSCode Extension Development / Node.js / TypeScript

⚡ Fun fact **I grew up on the country side in Germany next to 🐮 and 🐔

Skills

c05:
[person] (@[person]). base in Santa Clara, CA; works with @BentoML.

CNCF Ambassador & KubeVela Maintainer (,,• ₃ •,,)

Representative projects and topics: A self-evolving personal AI assistant.; The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!; Declarative Workflow of KubeVela which can run as standalone.; A cloud-native Pipeline resource.; The Modern Application Platform.; Docker for Your ML/DL Models Based on OCI Artifacts.

[person]

I'm currently working on KubeVela & BentoML :sparkles:.

c06:
[person] (@[person]). base in Liège (Belgium); works with Georode.

Earth scientist, software developer, musician

Representative projects and topics: N-D labeled arrays and datasets in Python; Interactive audio in Jupyter; Xarray extension and framework for computer model simulations; A fast, versatile and user-friendly landscape evolution model; Publish Xarray Datasets via a REST API.; Manipulation and analysis of geometric objects on the sphere..

c07:
[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.

c08: Recent work in ai and machine learning includes 'Task Aware Modulation Using Representation Learning for Upsaling of Terrestrial Carbon Fluxes'. Accurately upscaling terrestrial carbon fluxes is central to estimating the global carbon budget, yet remains challenging due to the sparse and regionally biased distribution of ground measurements.

c09:
[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.

c10:
[person] (@[person]). base in London, United Kingdom; works with @pydantic.

The Pydantic Stack: Pydantic Validation, Pydantic AI and Pydantic Logfire

Representative projects and topics: Data validation using Python type hints; AI Agent Framework, the Pydantic way; A minimal, secure Python interpreter written in Rust for use by AI; AI observability platform for production LLM and agent systems.; Simple, modern and fast file watching and code reload for Python, written in Rust; Dev tools for python.

c11:
[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

c12:
[person] (@[person]). base in ABQ.

Lead developer on @sympy. Doing math and Python.

Representative projects and topics: A Python REPL the way I like it; The git workflow for contributing to open source repositories.; A collection of a bunch of my .files; Tool to automatically replace 'import *' in Python files with explicit imports; A computer algebra system written in pure Python.; Symbolic representation of matrix multiplication decompositions from Google DeepMind's AlphaEvolve paper.

c13:
[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].

c14: Recent work in computer vision includes 'Benchmarking PhD-Level Coding in 3D Geometric Computer Vision'. AI-assisted coding has rapidly reshaped software practice and research workflows, yet today's models still struggle to produce correct code for complex 3D geometric vision... OpenAlex concept tags include "computer science, artificial intelligence, computer vision, machine learning '.

c15: 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.

c16: Recent work in ai and machine learning includes 'Quantifying Cross-Modal Interactions in Multimodal Glioma Survival Prediction via InterSHAP: Evidence for Additive Signal Integration'. Multimodal deep learning for cancer prognosis is commonly assumed to benefit from synergistic cross-modal interactions, yet this assumption has not been directly tested in survival prediction settings...

c17: Recent work in ai and machine learning includes 'Aligned, Orthogonal or In-conflict: When can we safely optimize Chain-of-Thought?'. Chain-of-Thought (CoT) monitoring, in which automated systems monitor the CoT of an LLM, is a promising approach for effectively overseeing AI systems...

c18:
[person] (@[person]). base in Mountain View, California; works with Google.

Engineering Lead at Google working on Chrome & Web Platform

Representative projects and topics: ⚡Faster subsequent page-loads by prefetching in-viewport links during idle time; 📦 Workbox: JavaScript libraries for Progressive Web Apps; Extract & Inline Critical-path CSS in HTML pages; Material Design Components in HTML/CSS/JS; Helping you select a JavaScript framework - Todo apps for React.js, Angular, Vue and many more; Yeoman - a set of tools for automating development workflow.

🔭 I’m currently working on AI at Google (Gemini, Agents, Vertex)
👯 I’m looking to collaborate on Developer Tooling
🤔 I’m looking for help with Framework collaborations
💬 Ask me about anything

c19:
[person] (@[person]). base in Schaumburg, IL; works with @PrefectHQ.

Software Engineer @PrefectHQ

Representative projects and topics: Prefect is a workflow orchestration framework for building resilient data pipelines in Python.; An experiment in distributed memoization; MCP server for delegating tasks to Devin AI; Generate MDX API documentation from Python modules; Test project for `include_files` feature with the `prefect-aws` `@ecs` decorator; 🫏 Add a stubborn streak to your code..

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: Public author profile is associated with University of Salento. Recent work in quantitative social science and economics includes 'Violence Against Women: a pilot study on the perception of Apulian High school students'. Violence Against Women (VAW) is a widespread issue deeply rooted in social and cultural structures... OpenAlex concept tags include "medicine, biology, mathematics, computer science '.

c00: 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.

c01:
[person] (@[person]). base in Austria; works with Earendil.

Software developer and Open Source nut. Creator of the Flask framework. Founder of @earendil-works. Other things of interest: @pallets and @getsentry

Representative projects and topics: The Python micro framework for building web applications.; MiniJinja is a powerful but minimal dependency template engine for Rust compatible with Jinja/Jinja2; A snapshot testing library for rust; A very fast and expressive template engine.; Python composable command line interface toolkit; These are commands I use with agents, mostly Claude.

c02:
[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.

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: Recent work in computational materials and physics includes 'Learning the Exact Flux: Neural Riemann Solvers with Hard Constraints'. Godunov-type methods, which obtain numerical fluxes through local Riemann problems at cell interfaces, are among the most fundamental and widely used numerical methods in computational fluid dynamics... OpenAlex concept tags include "storm, computer science, meteorology, shallow water equations '.

c05:
[person] (@[person]). base in Bangalore, India; works with @google.

Software Engineer @google

Representative projects and topics: Yahoo! Cloud Serving Benchmark; Implementation of the paper - IPACT: A Dynamic Protocol for an Ethernet PON (EPON) by Glen Kramer and Biswanath Mukherjee; Run program in a sandbox-like environment; Use C# instead of YAML to define your Azure DevOps pipelines; A library to create QueryExpression the fluent way; CSharpier is an opinionated code formatter for c#..

c06: Recent work in quantitative social science and economics includes 'Longest weakly increasing subsequences of discrete random walks on the integers with heavy tailed distribution of increments'. We investigate the behavior of the length of the longest weakly increasing subsequences (weak LIS) of $n$-step random walks with nonzero integer increments $k = \pm 1, \pm 2, \dots$ given by a zero-mean, symmetric heavy tailed mass distribution proportional to $|k|^{-1-α}$ for several values of the real parameter $α> 0$ together with that of the simple random walk ($k=\pm 1$), to which the $n$-step heavy tailed random walks tends when $α> (1+o(1))\log_{2}{n}$... OpenAlex concept tags include "poisson distribution, statistics, statistical physics, random variable '.

c07:
[person] (@[person]). base in Liège (Belgium); works with Georode.

Earth scientist, software developer, musician

Representative projects and topics: N-D labeled arrays and datasets in Python; Interactive audio in Jupyter; Xarray extension and framework for computer model simulations; A fast, versatile and user-friendly landscape evolution model; Publish Xarray Datasets via a REST API.; Manipulation and analysis of geometric objects on the sphere..

c08:
[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.

c09: YC batch S2020 founder at Humanloop. Role: Founder. Ex-MonolithAI and Google. ML PhD @ UCL and MSc in Physics @ Cambridge.

c10:
[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":"

c11:
[person] (@[person]). base in Schaumburg, IL; works with @PrefectHQ.

Software Engineer @PrefectHQ

Representative projects and topics: Prefect is a workflow orchestration framework for building resilient data pipelines in Python.; An experiment in distributed memoization; MCP server for delegating tasks to Devin AI; Generate MDX API documentation from Python modules; Test project for `include_files` feature with the `prefect-aws` `@ecs` decorator; 🫏 Add a stubborn streak to your code..

c12:
[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.

c13: 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.

c14:
[person] (@[person]). base in Amsterdam; works with @pydantic.

[contact removed]

Representative projects and topics: Data validation using Python type hints; A flake8 plugin to check Pydantic related code.; Automatically add type hints for Django powered applications.; An extensible and sane implementation of JsonLogic; A Python library for tree data structures with an intuitive, yet powerful API.; PyPy is a very fast and compliant implementation of the Python language..

c15:
[person] (@[person]). base in ABQ.

Lead developer on @sympy. Doing math and Python.

Representative projects and topics: A Python REPL the way I like it; The git workflow for contributing to open source repositories.; A collection of a bunch of my .files; Tool to automatically replace 'import *' in Python files with explicit imports; A computer algebra system written in pure Python.; Symbolic representation of matrix multiplication decompositions from Google DeepMind's AlphaEvolve paper.

c16: YC batch S2020 founder at Humanloop. Role: Co-founder and CTO. Previously Co-founder @Certua. ML PhD at UCL and BSc Mathematics at Trinity.

c17:
[person] (@[person]). base in Chicago; works with Canonical.

open science enthusiast ~ software developer ~ an under the sea code monkey

Representative projects and topics: The snapd and snap tools enable systems to work with .snap files.; PMR ( configs used by CheckBox developers; Creating delicious APIs for Django apps since 2010. (fork); toy app to demonstrate some tastypie behavior; The official website for the Ubuntu operating system; A tool to create Ubuntu images for embedded systems - mainly ARM/ARM64 boards like Beaglebone, RaspberryPi 2, RaspberryPI 3 or 96Boards DragonBoard410c.

c18:
[person] (@[person]). works with @langchain-ai.

Trace tackler @ LangChain

Representative projects and topics: An Open-Source Asynchronous Coding Agent; 📃 A better UX for chat, writing content, and coding with LLMs.; An open-source, no-code agent building platform.; 📲 An agent for sourcing, curating, and scheduling social media posts with human-in-the-loop.; 🦜💬 Web app for interacting with any LangGraph agent (PY & TS) via a chat interface.; 📥 An inbox UX for interacting with human-in-the-loop agents..

[](

c19: Recent work in computational biology includes 'Quantifying Memorization and Privacy Risks in Genomic Language Models'. Genomic language models (GLMs) have emerged as powerful tools for learning representations of DNA sequences, enabling advances in variant prediction, regulatory element identification, and cross-task transfer learning.

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: Public author profile is associated with University of Manchester. Recent work in computational materials and physics includes 'Deep-UV bleaching of charge disorder in encapsulated graphene'. Disorder masks much of the rich physics in two-dimensional electronic systems, with charged impurities often the limiting factor... OpenAlex concept tags include "materials science, nanotechnology, chemistry, physics '.

c00:
[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

c01:
[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.

c02:
[person] (@[person]). base in London, United Kingdom; works with @pydantic.

The Pydantic Stack: Pydantic Validation, Pydantic AI and Pydantic Logfire

Representative projects and topics: Data validation using Python type hints; AI Agent Framework, the Pydantic way; A minimal, secure Python interpreter written in Rust for use by AI; AI observability platform for production LLM and agent systems.; Simple, modern and fast file watching and code reload for Python, written in Rust; Dev tools for python.

c03:
[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.

c04:
[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.

c05:
[person] (@[person]). base in Austria; works with Earendil.

Software developer and Open Source nut. Creator of the Flask framework. Founder of @earendil-works. Other things of interest: @pallets and @getsentry

Representative projects and topics: The Python micro framework for building web applications.; MiniJinja is a powerful but minimal dependency template engine for Rust compatible with Jinja/Jinja2; A snapshot testing library for rust; A very fast and expressive template engine.; Python composable command line interface toolkit; These are commands I use with agents, mostly Claude.

c06:
[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.

c07: YC batch W2024 founder at Ecliptor. Role: Founder. EECS from the University of Michigan, concentrated in machine learning + system and product design. Before founding Ecliptor, ML engineer on the Recommendations team at Twitch, and prior internships in product @ Nutanix and IoT Eng @ GE.

c08: Recent work in ai and machine learning includes 'Aligned, Orthogonal or In-conflict: When can we safely optimize Chain-of-Thought?'. Chain-of-Thought (CoT) monitoring, in which automated systems monitor the CoT of an LLM, is a promising approach for effectively overseeing AI systems...

c09:
[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:

c10:
[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.

c11:
[person] (@[person]). base in Liège (Belgium); works with Georode.

Earth scientist, software developer, musician

Representative projects and topics: N-D labeled arrays and datasets in Python; Interactive audio in Jupyter; Xarray extension and framework for computer model simulations; A fast, versatile and user-friendly landscape evolution model; Publish Xarray Datasets via a REST API.; Manipulation and analysis of geometric objects on the sphere..

c12: Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. Drug discovery relies on iterative expert workflows that are slow to parallelize and difficult to scale...

c13:
[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

c14: Recent work in computational biology includes 'Quantifying Memorization and Privacy Risks in Genomic Language Models'. Genomic language models (GLMs) have emerged as powerful tools for learning representations of DNA sequences, enabling advances in variant prediction, regulatory element identification, and cross-task transfer learning.

c15:
[person] (@[person]). base in Half Moon Bay, California; works with Datasette.

Representative projects and topics: An open source multi-tool for exploring and publishing data; Python CLI utility and library for manipulating SQLite databases; Access large language models from the command-line; A command-line utility for taking automated screenshots of websites; Concatenate a directory full of files into a single prompt for use with LLMs; A tool for creating credentials for accessing S3 buckets.

c16: Recent work in quantitative social science and economics includes 'Longest weakly increasing subsequences of discrete random walks on the integers with heavy tailed distribution of increments'. We investigate the behavior of the length of the longest weakly increasing subsequences (weak LIS) of $n$-step random walks with nonzero integer increments $k = \pm 1, \pm 2, \dots$ given by a zero-mean, symmetric heavy tailed mass distribution proportional to $|k|^{-1-α}$ for several values of the real parameter $α> 0$ together with that of the simple random walk ($k=\pm 1$), to which the $n$-step heavy tailed random walks tends when $α> (1+o(1))\log_{2}{n}$... OpenAlex concept tags include "poisson distribution, statistics, statistical physics, random variable '.

c17:
[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

c18:
[person] (@[person]). base in Cambridge, MA.

Engineering @langchain-ai

Representative projects and topics: The agent engineering platform; Build resilient language agents as graphs.; Agent harness built with LangChain and LangGraph. Equipped with a planning tool, a filesystem backend, and the ability to spawn subagents - well-equipped to handle complex agentic tasks.; 🦜🔗 Docs for LangChain projects; Webhook trigger option for LangSmith's Fleet; ID vinyl records with Computer Vision & fetch metadata for radio DJs.

c19:
[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.

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