source: Public author profile is associated with Oak Ridge National Laboratory. Recent work in ai and machine learning includes 'Tucker Attention: A generalization of approximate attention mechanisms'. Generalizes memory-efficient attention variants like grouped-query and multi-head latent attention via Tucker tensor decomposition. OpenAlex concept tags include "computer science, mathematical optimization, physics, mathematics '.

c00:
[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.

c01: 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.

c02:
[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 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

c05:
[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

c06: 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'. Grows wafer-scale, electrically tunable O-band InAs/InGaAs quantum dots on GaAs by molecular beam epitaxy for quantum photonics. OpenAlex concept tags include "optics, physics, electron, engineering '.

c07:
[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:
[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.

c12: YC batch S2023 founder at Automorphic. Role: Founder. When not working on Automorphic, building AI to beat me at chess.

c13: YC batch W2026 founder at Captain. Role: Co-Founder and CTO. CTO @ Captain | Published AI NLP Researcher | Built OCR engines @ Boar's Head | Scaled high-performance RAG pipelines for the past 4 years | Built a Cross Region L2 Datacenter

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 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.

c16:
[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..

c18:
[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.

c19: Recent work in computational biology includes 'Quantifying Memorization and Privacy Risks in Genomic Language Models'. Measures how much genomic language models memorize training DNA sequences and the resulting privacy risk.

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 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'. Grows wafer-scale, electrically tunable O-band InAs/InGaAs quantum dots on GaAs by molecular beam epitaxy for quantum photonics. OpenAlex concept tags include "physics, materials science, optics, optoelectronics '.

c00:
[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.

c01: 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.

c02:
[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.

c03: YC batch S2020 founder at Humanloop. Role: Co-founder and CTO. Previously Co-founder @Certua. ML PhD at UCL and BSc Mathematics at Trinity.

c04: YC batch S2023 founder at Parea. Role: Founder. Senior Software Engineer at Lyft. J.P. Morgan TMT Investment Banking. BA @ Columbia University. MSc @ New York University

c05: YC batch W2023 founder at Infisical. Role: Founder. Co-founder/CEO of Infisical. Formerly Figma, Cornell, and bunq.

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: 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.

c08:
[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).

c09: 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.

c10: YC batch W2023 founder at Langfuse. Role: Founder. Previously worked with founder-CEOs of German Fintech Unicorn Scalable Capital. Studied Economic History, dropped out of a PhD at Oxford, and was a competitive wine taster.

c11: Recent work in ai and machine learning includes 'Task Aware Modulation Using Representation Learning for Upsaling of Terrestrial Carbon Fluxes'. Uses task-aware representation learning to upscale sparse ground carbon-flux measurements to broader terrestrial coverage.

c12:
[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..

c13:
[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.

c14:
[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.

c15: 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

c16:
[person] (@[person]). works with @NVIDIA.

I like puns.

Representative projects and topics: A modal TUI for tracking home projects, maintenance schedules, appliances, and vendor quotes.; the portable Python dataframe library; Protocol Buffers for the rest of us; Run Numba compiled functions in SQLite; Time everything in IPython; Continuous background code review database for agents, work faster and smarter with accountability for every line of generated code..

c17: 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.

c18:
[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.

c19: YC batch W2023 founder at Infisical. Role: Co-founder. Co-founder of Infisical.

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 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 '.

c00:
[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.

c01:
[person] (@[person]). base in Salt Lake City, Utah, USA; works with @epicweb-dev.

Improving 🌎 with quality software · Husband, 6x Dad, Latter-day Saint, Dev Educator, MVP ⚡ EpicAI.pro 🌌 EpicWeb.dev 🚀 EpicReact.dev

Representative projects and topics: My personal website; The workshop app for all workshops on EpicWeb.dev; 🐐 Simple and complete React DOM testing utilities that encourage good testing practices.; 🦤 Give me MDX/TSX strings and I'll give you back a component you can render. Supports imports!; An RSS feed generator for media (audio/video).; 🔀 Cross platform setting of environment scripts.

alt="Helping people make the world a better place through quality software"
src="
/>

Learn more about me

c02: 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 "medicine, biology, internal medicine, pathology '.

c03:
[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":"

c04:
[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.

c05:
[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:

c06:
[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.

c07: YC batch S2020 founder at Humanloop. Role: Co-founder and CTO. Previously Co-founder @Certua. ML PhD at UCL and BSc Mathematics at Trinity.

c08:
[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

c09:
[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:.

c10:
[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..

c11: Recent work in computational biology includes 'Quantifying Memorization and Privacy Risks in Genomic Language Models'. Measures how much genomic language models memorize training DNA sequences and the resulting privacy risk.

c12: Recent work in ai and machine learning includes 'Do covariates explain why these groups differ? The choice of reference group can reverse conclusions in the Oaxaca-Blinder decomposition'. Shows the reference group chosen in Oaxaca-Blinder decomposition can flip conclusions about what explains between-group outcome gaps. OpenAlex concept tags include "psychology, public economics, outbreak, philosophy '.

c13:
[person] (@[person]). base in Bengaluru; works with @pydantic.

Software Engineer @pydantic

Representative projects and topics: Agent Framework / shim to use Pydantic with LLMs; AI observability platform for production LLM and agent systems.; Uncomplicated Observability for Python and beyond! 🪵🔥; JavaScript and TypeScript SDK for Logfire.

c14:
[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..

c15: 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.

c16: 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 "medicine, materials science, computer science, optics '.

c17:
[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.

c18: 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 '.

c19: Recent work in ai and machine learning includes 'Do covariates explain why these groups differ? The choice of reference group can reverse conclusions in the Oaxaca-Blinder decomposition'. Shows the reference group chosen in Oaxaca-Blinder decomposition can flip conclusions about what explains between-group outcome gaps. OpenAlex concept tags include "computer science, mathematics, bayesian probability, statistics '.

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 Monash University. Recent work in quantitative social science and economics includes 'When Bayes goes bad: Weakly-regularized covariate adjustment leads to a biased estimate of prevalence'. Shows weak regularization in Bayesian covariate-adjusted prevalence estimation from non-random samples can bias the result. OpenAlex concept tags include "econometrics, bayesian probability, mathematics, artificial intelligence '.

c00: 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.

c01: YC batch S2023 founder at Automorphic. Role: Founder. When not working on Automorphic, building AI to beat me at chess.

c02:
[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.

c03:
[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.

c04:
[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: 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.

c06:
[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:

c07: 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.

c08: 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'. Grows wafer-scale, electrically tunable O-band InAs/InGaAs quantum dots on GaAs by molecular beam epitaxy for quantum photonics. OpenAlex concept tags include "quantum dot, physics, optoelectronics, materials science '.

c09:
[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

c10: 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.

c11: 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.

c12: 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.

c14:
[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.

c15:
[person] (@[person]). base in California; works with @carbonplan.

Software/Data Engineer @carbonplan | Core Maintainer of xarray @pydata

Representative projects and topics: N-D labeled arrays and datasets in Python; An intake plugin for parsing an Earth System Model (ESM) catalog and loading assets into xarray datasets.; Parallel computing with task scheduling; The Xarray landing page; Climate downscaling using CMIP6 data; A small utility for generating ND array pyramids using Xarray and Zarr..

Coucou 👋 🌎

As a software engineer, it has been extremely rewarding for me to work across institutional and geographical boundaries to collaborate on research tools and software. I am currently employed by @CarbonPlan, a non-profit working on open data science tools and climate policy issues surrounding carbon removal and climate solutions.

Also, I am an advocate for and contributor to open-source software. In the scientific Python data ecosystem, I help maintain Xarray, dask-jobqueue, and a few other packages.

📝 Latest Blog Posts

« Multitude de Stromae » - Des morceaux aussi forts sur des sujets si primordials 🎶
Dictionary Merge and Update Operators in Python 3.9
« Mesdames » - Parce que l’avenir appartient à celles qu’on aime trop 🎶
How to Generate Wildcard SSL Certificate using Let’s Encrypt/Certbot on CentOS
🚀 Xarray v0.17.0 is here!

More on blog.andersonbanihirwe.dev

🎥 Latest YouTube Videos

Santé 🥂 🍻

c16:
[person] (@[person]). base in Salt Lake City, Utah, USA; works with @epicweb-dev.

Improving 🌎 with quality software · Husband, 6x Dad, Latter-day Saint, Dev Educator, MVP ⚡ EpicAI.pro 🌌 EpicWeb.dev 🚀 EpicReact.dev

Representative projects and topics: My personal website; The workshop app for all workshops on EpicWeb.dev; 🐐 Simple and complete React DOM testing utilities that encourage good testing practices.; 🦤 Give me MDX/TSX strings and I'll give you back a component you can render. Supports imports!; An RSS feed generator for media (audio/video).; 🔀 Cross platform setting of environment scripts.

alt="Helping people make the world a better place through quality software"
src="
/>

Learn more about me

c17: YC batch S2020 founder at Humanloop. Role: Founder. Machine Learning and Engineering at Cambridge and MIT.

c18: YC batch F2024 founder at Candle. Role: Co-Founder and CTO. Previously worked on ads relevance & ranking @ Twitter, and architected changes to the sharing/permissions framework @ Asana.

c19: YC batch S2020 founder at Humanloop. Role: Founder. Ex-MonolithAI and Google. ML PhD @ UCL and MSc in Physics @ Cambridge.

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 Weizmann Institute of Science. Recent work in computational biology includes 'Modeling the mutational dynamics of very short tandem repeats'. Models how very short tandem-repeat sequences in the genome mutate and expand or contract over generations. OpenAlex concept tags include "biology, stem cell, computational biology, genetics '.

c00: 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.

c01:
[person] (@[person]). base in California; works with @carbonplan.

Software/Data Engineer @carbonplan | Core Maintainer of xarray @pydata

Representative projects and topics: N-D labeled arrays and datasets in Python; An intake plugin for parsing an Earth System Model (ESM) catalog and loading assets into xarray datasets.; Parallel computing with task scheduling; The Xarray landing page; Climate downscaling using CMIP6 data; A small utility for generating ND array pyramids using Xarray and Zarr..

Coucou 👋 🌎

As a software engineer, it has been extremely rewarding for me to work across institutional and geographical boundaries to collaborate on research tools and software. I am currently employed by @CarbonPlan, a non-profit working on open data science tools and climate policy issues surrounding carbon removal and climate solutions.

Also, I am an advocate for and contributor to open-source software. In the scientific Python data ecosystem, I help maintain Xarray, dask-jobqueue, and a few other packages.

📝 Latest Blog Posts

« Multitude de Stromae » - Des morceaux aussi forts sur des sujets si primordials 🎶
Dictionary Merge and Update Operators in Python 3.9
« Mesdames » - Parce que l’avenir appartient à celles qu’on aime trop 🎶
How to Generate Wildcard SSL Certificate using Let’s Encrypt/Certbot on CentOS
🚀 Xarray v0.17.0 is here!

More on blog.andersonbanihirwe.dev

🎥 Latest YouTube Videos

Santé 🥂 🍻

c02: Recent work in quantitative social science and economics includes 'Association of Radiologic PPFE Change with Mortality in Lung Cancer Screening Cohorts'. Tracks radiologic pleuroparenchymal fibroelastosis changes in lung-cancer screening cohorts and tests their association with mortality.

c03: 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.

c04:
[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.

c05: YC batch W2023 founder at Infisical. Role: Founder. Co-founder of Infisical.

c06: 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.

c07: YC batch S2020 founder at Humanloop. Role: Founder. Machine Learning and Engineering at Cambridge and MIT.

c08:
[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.

c09: YC batch W2023 founder at Infisical. Role: Co-founder. Co-founder of Infisical.

c10: Recent work in ai and machine learning includes 'Task Aware Modulation Using Representation Learning for Upsaling of Terrestrial Carbon Fluxes'. Uses task-aware representation learning to upscale sparse ground carbon-flux measurements to broader terrestrial coverage.

c11:
[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

c12:
[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

c13:
[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.

c14: 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.

c15:
[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..

[](

c16: YC batch W2023 founder at Infisical. Role: Founder. Co-founder/CEO of Infisical. Formerly Figma, Cornell, and bunq.

c17:
[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.

c18: YC batch S2025 founder at Modelence. Role: Co-Founder & CTO. An engineer and engineering manager with 18 years of experience. Was the founding engineer and Director of Engineering at CodeSignal.

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: Recent work in ai and machine learning includes 'Do covariates explain why these groups differ? The choice of reference group can reverse conclusions in the Oaxaca-Blinder decomposition'. Shows the reference group chosen in Oaxaca-Blinder decomposition can flip conclusions about what explains between-group outcome gaps. OpenAlex concept tags include "computer science, mathematics, bayesian probability, statistics '.

c00: YC batch W2023 founder at Infisical. Role: Founder. Co-founder/CEO of Infisical. Formerly Figma, Cornell, and bunq.

c01: 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'. Grows wafer-scale, electrically tunable O-band InAs/InGaAs quantum dots on GaAs by molecular beam epitaxy for quantum photonics. OpenAlex concept tags include "optics, physics, electron, engineering '.

c02: 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.

c03: YC batch W2023 founder at Langfuse. Role: Co-Founder & CEO. Diverse experience across Product, Sales, BI and full-stack engineering at Google, DHL, and early stage startups. Masters in Management and Computer Science from Technical University Munich.

c04: 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'. Grows wafer-scale, electrically tunable O-band InAs/InGaAs quantum dots on GaAs by molecular beam epitaxy for quantum photonics. OpenAlex concept tags include "quantum dot, photon, optoelectronics, physics '.

c05: 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'. Grows wafer-scale, electrically tunable O-band InAs/InGaAs quantum dots on GaAs by molecular beam epitaxy for quantum photonics. OpenAlex concept tags include "physics, quantum dot, photon, optoelectronics '.

c06: 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.

c07: YC batch W2020 founder at PostHog. Role: Founder. Cofounder at PostHog. Used to be a bad pro cyclist. Knows about brand, shipping lots of stuff, marketing to developers.

c08: YC batch W2023 founder at Resend. Role: Founder. Co-founder & CTO at Resend. 1st eng hire at WorkOS (Series B, valued at $500M). Built 100+ node packages and 2 products featured #1 and #4 on Product Hunt.

c10: YC batch W2026 founder at CellType. Role: Co-Founder & CEO. Co-Founder & CEO of CellType (W26). Yale Professor, 11,000+ citations. Built biological foundation models with Google. Building the agentic drug company.

c11: 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.

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:
[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 Depot. Role: Co-founder and CTO. Cofounder and CTO of Depot. US expat living in the UK.

c15:
[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

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 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.

c18: YC batch W2020 founder at PostHog. Role: Founder. Co-founder at PostHog.

c19: YC batch W2023 founder at Trigger.dev. Role: Co-founder. Co-founder of Trigger.dev; the open source background jobs platform, and JSON Hero.

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 Erasmus MC. 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 "medicine, computer science, computer vision, artificial intelligence '.

c00:
[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:

c01:
[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.

c02: 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.

c03:
[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.

c04:
[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.

c05:
[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.

c06:
[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]

c07:
[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

c08:
[person] (@[person]). works with @NVIDIA.

I like puns.

Representative projects and topics: A modal TUI for tracking home projects, maintenance schedules, appliances, and vendor quotes.; the portable Python dataframe library; Protocol Buffers for the rest of us; Run Numba compiled functions in SQLite; Time everything in IPython; Continuous background code review database for agents, work faster and smarter with accountability for every line of generated code..

c09: Recent work in ai and machine learning includes 'Do covariates explain why these groups differ? The choice of reference group can reverse conclusions in the Oaxaca-Blinder decomposition'. Shows the reference group chosen in Oaxaca-Blinder decomposition can flip conclusions about what explains between-group outcome gaps. OpenAlex concept tags include "computer science, mathematics, bayesian probability, statistics '.

c10:
[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":"

c11:
[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.

c12:
[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":"

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: YC batch S2023 founder at Automorphic. Role: Founder. Building the next generation of artificial narrow intelligence.

c16: Recent work in ai and machine learning includes 'Do covariates explain why these groups differ? The choice of reference group can reverse conclusions in the Oaxaca-Blinder decomposition'. Shows the reference group chosen in Oaxaca-Blinder decomposition can flip conclusions about what explains between-group outcome gaps. OpenAlex concept tags include "psychology, public economics, outbreak, philosophy '.

c17:
[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.

c18: YC batch S2023 founder at Automorphic. Role: Founder. Building AI agents that can remember and self-improve at Automorphic.

c19: 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'. Analyzes the length of longest weakly increasing subsequences in random walks whose step sizes follow a heavy-tailed distribution. OpenAlex concept tags include "poisson distribution, statistics, statistical physics, random variable '.

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