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

c00:
[person] (@[person]). base in Minneapolis; works with University of Washington.

Open source developer, trained in phonetics and auditory neuroscience.

Representative projects and topics: MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python; A clean, three-column Sphinx theme with Bootstrap for the PyData community; PHOIBLE data and development.; Praat scripts for streamlining manual measurements in acoustic analysis; tools for phoneticians and phonologists; the scripts and files used to generate my personal website.

I am a developer of open-source scientific software, and a scientist trained in acoustic phonetics, speech perception, and auditory neuroscience.
My scientific interest broadly centers on the perception and representation of speech sounds.
I'm (probably) most known for my work on MNE-Python.

c01: 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 "nanowire, materials science, physics, optoelectronics '.

c02:
[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:.

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

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

🏍 🛣 🌎 I miss _why.

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

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

c05: Public author profile is associated with 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, materials science, optoelectronics, optics '.

c06: 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 "quantum dot, physics, photon, quantum '.

c07: 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.

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..

c10:
[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.

c11: 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.

c12: 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 '.

c13: 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 '.

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])

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.

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:
[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:
[person] (@[person]). base in New York; works with Dagster Labs.

Founder of Dagster Labs. Ex-Facebook Engineer. GraphQL co-creator.

Representative projects and topics: Python framework for building scalable GraphQL servers; 📚 Parameterize, execute, and analyze notebooks; The best way to create REST APIs - Generate RESTful APIs from your GraphQL Server; An orchestration platform for the development, production, and observation of data assets..

c19:
[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

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 Santobono Children's Hospital. Recent work in quantitative social science and economics includes 'Violence Against Women: a pilot study on the perception of Apulian High school students'. A pilot survey of how Apulian high-school students perceive violence against women. OpenAlex concept tags include "biology, mathematics, environmental science, statistics '.

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

Creating tech content

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

Hi there 👋, I am Sherlock.

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

c01: 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.

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: Recent work in computational materials and physics includes 'Learning the Exact Flux: Neural Riemann Solvers with Hard Constraints'. Trains a neural network to approximate exact Riemann-problem fluxes for Godunov-type computational fluid dynamics solvers. OpenAlex concept tags include "storm, computer science, meteorology, shallow water equations '.

c04: Recent work in computational biology includes 'Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction'. Finds that sampling the loss landscape at intermediate Langevin-dynamics temperatures best trains transformers for protein structure prediction. OpenAlex concept tags include "medicine, chemistry, internal medicine, biochemistry '.

c05:
[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.

c06: Recent work in computer vision includes 'Video Models Reason Early: Exploiting Plan Commitment for Maze Solving'. Finds video diffusion models commit to a maze-solving plan early in generation, exploited to speed up reasoning. OpenAlex concept tags include "computer science, artificial intelligence, machine learning, task (project management) '.

c07: YC batch S2020 founder at Humanloop. Role: Founder. Ex-MonolithAI and Google. ML PhD @ UCL and MSc in Physics @ Cambridge.

c08: YC batch S2020 founder at Humanloop. Role: Founder. Machine Learning and Engineering at Cambridge and MIT.

c09: [person]

c10: [person]

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

c13: Recent work in ai and machine learning includes 'Aligned, Orthogonal or In-conflict: When can we safely optimize Chain-of-Thought?'. Studies when optimizing an LLM's visible chain-of-thought stays a faithful monitor of its reasoning versus becomes misleading.

c14:
[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:

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])

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:

c17: 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 '.

c18:
[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.

c19: 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

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 '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.

c00:
[person] (@[person]). base in New York; works with Dagster Labs.

Founder of Dagster Labs. Ex-Facebook Engineer. GraphQL co-creator.

Representative projects and topics: Python framework for building scalable GraphQL servers; 📚 Parameterize, execute, and analyze notebooks; The best way to create REST APIs - Generate RESTful APIs from your GraphQL Server; An orchestration platform for the development, production, and observation of data assets..

c01:
[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].

c02:
[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..

c03: 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 '.

c04:
[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

c05: Public author profile is associated with Central South University. Recent work in computer vision includes 'OmniRoam: World Wandering via Long-Horizon Panoramic Video Generation'. Generates long-horizon, navigable panoramic video to model persistent, explorable 3D scenes. OpenAlex concept tags include "computer science, artificial intelligence, computer vision, image (mathematics) '.

c06: Public author profile is associated with Yeshiva University. Recent work in computational biology includes 'Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design'. An autonomous agent that plans and runs de novo drug-design iterations, validated against real wet-lab results. OpenAlex concept tags include "medicine, computer science, physics, optics '.

c07: Public author profile is associated with Universidade de São Paulo. 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 "physics, mathematics, discrete mathematics, computer science '.

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

c09: 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.

c10: Public author profile is associated with NewYork–Presbyterian Hospital. 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 "medicine, internal medicine, chemistry, biology '.

c11: 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 "materials science, physics, optoelectronics, optics '.

c12: 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, materials science, optoelectronics, optics '.

c13:
[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]

c14: 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 '.

c15: 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 "molecular beam epitaxy, optoelectronics, materials science, epitaxy '.

c16: Public author profile is associated with Austin College. Recent work in computational biology includes 'Poisoning the Genome: Targeted Backdoor Attacks on DNA Foundation Models'. Demonstrates targeted backdoor data-poisoning attacks against genomic foundation models trained on DNA sequences. OpenAlex concept tags include "computer science, genome, sequence (biology), dna sequencing '.

c17: 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 '.

c18: 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 "augmented reality, medicine, computer science, artificial intelligence '.

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

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

c00:
[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..

c01: 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.

c02:
[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.

c03:
[person] (@[person]). base in New York; works with Dagster Labs.

Founder of Dagster Labs. Ex-Facebook Engineer. GraphQL co-creator.

Representative projects and topics: Python framework for building scalable GraphQL servers; 📚 Parameterize, execute, and analyze notebooks; The best way to create REST APIs - Generate RESTful APIs from your GraphQL Server; An orchestration platform for the development, production, and observation of data assets..

c04: Recent work in quantitative social science and economics includes 'Penalized GMM Framework for Inference on Functionals of Nonparametric Instrumental Variable Estimators'. A penalized GMM method for debiased statistical inference on functionals of nonparametric instrumental-variable estimators. OpenAlex concept tags include "estimator, mathematics, computer science, mathematical optimization '.

c05:
[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.

c06:
[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.

c07: Recent work in quantitative social science and economics includes 'GPU-accelerated Bayesian inference for block-cave mine monitoring via muon tomography'. Speeds up Bayesian inversion of muon-tomography data with GPUs to monitor rock density in block-cave mining. OpenAlex concept tags include "computer science, linguistics, geography, archaeology '.

c08:
[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.

c09: 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.

c10: YC batch S2020 founder at Humanloop. Role: Founder. Ex-MonolithAI and Google. ML PhD @ UCL and MSc in Physics @ Cambridge.

c11:
[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..

c12:
[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:

c13: 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 '.

c14:
[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.

c15: 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.

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 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#..

c18:
[person] (@[person]). base in Minneapolis; works with University of Washington.

Open source developer, trained in phonetics and auditory neuroscience.

Representative projects and topics: MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python; A clean, three-column Sphinx theme with Bootstrap for the PyData community; PHOIBLE data and development.; Praat scripts for streamlining manual measurements in acoustic analysis; tools for phoneticians and phonologists; the scripts and files used to generate my personal website.

I am a developer of open-source scientific software, and a scientist trained in acoustic phonetics, speech perception, and auditory neuroscience.
My scientific interest broadly centers on the perception and representation of speech sounds.
I'm (probably) most known for my work on MNE-Python.

c19:
[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..

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 Alexandru Ioan Cuza University. Recent work in ai and machine learning includes 'Aligning Validation with Deployment: Target-Weighted Cross-Validation for Spatial Prediction'. Proposes a target-weighted cross-validation scheme so spatial-prediction error estimates match the actual deployment distribution. OpenAlex concept tags include "geology, computer science, environmental science, geography '.

c00:
[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 🥂 🍻

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

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

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

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

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

c02:
[person] (@[person]). base in Minneapolis; works with University of Washington.

Open source developer, trained in phonetics and auditory neuroscience.

Representative projects and topics: MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python; A clean, three-column Sphinx theme with Bootstrap for the PyData community; PHOIBLE data and development.; Praat scripts for streamlining manual measurements in acoustic analysis; tools for phoneticians and phonologists; the scripts and files used to generate my personal website.

I am a developer of open-source scientific software, and a scientist trained in acoustic phonetics, speech perception, and auditory neuroscience.
My scientific interest broadly centers on the perception and representation of speech sounds.
I'm (probably) most known for my work on MNE-Python.

c03: Recent work in quantitative social science and economics includes 'A Unified Framework for Nonlinear Mediation Analysis of Random Objects'. Extends mediation analysis to non-Euclidean data like distributions, images, and networks using a nonlinear geometric framework. OpenAlex concept tags include "medicine, biology, cancer, internal medicine '.

c04: 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 '.

c05:
[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.

c06:
[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.

c07:
[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

c08:
[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..

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]). 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.

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 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.

c13:
[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#..

c14:
[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

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: 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.

c17:
[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.

c18: 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.

c19:
[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.

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 Technical University of Munich. Recent work in quantitative social science and economics includes 'Tree models for covariate-dependent method agreement with repeated measurements in clinical research'. Uses tree models to let Bland-Altman measurement-agreement analysis vary with covariates under repeated clinical measurements. OpenAlex concept tags include "medicine, internal medicine, biology, computer science '.

c00:
[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.

c02: Recent work in ai and machine learning includes 'Aligned, Orthogonal or In-conflict: When can we safely optimize Chain-of-Thought?'. Studies when optimizing an LLM's visible chain-of-thought stays a faithful monitor of its reasoning versus becomes misleading.

c03:
[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:.

c04: 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.

c05:
[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

c06:
[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

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]). 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.

c09: Recent work in computer vision includes 'OmniRoam: World Wandering via Long-Horizon Panoramic Video Generation'. Generates long-horizon, navigable panoramic video to model persistent, explorable 3D scenes. OpenAlex concept tags include "computer science, artificial intelligence, computer vision, computer graphics (images) '.

c10:
[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.

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]). 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.

c13:
[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":"

c14:
[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.

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 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.

c17: YC batch S2020 founder at Humanloop. Role: Founder. Ex-MonolithAI and Google. ML PhD @ UCL and MSc in Physics @ Cambridge.

c18: 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.

c19:
[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 🥂 🍻

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