{"schema_version":"onlylabs.public_analysis_evidence.v1","title":"Fireworks AI analysis evidence pack","description":"Public onlylabs evidence pack for cited agent analysis: captured pages, ranked public signals, and stored web-search provenance used by the background analysis workflow.","url":"https://onlylabs.fyi/labs/fireworks-ai","json_url":"https://onlylabs.fyi/analysis/fireworks-ai/evidence.json","generated_at":"2026-06-13T13:58:17.736Z","org":{"slug":"fireworks-ai","name":"Fireworks AI","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/fireworks-ai"},"analysis":null,"workflow":{"version":"onlylabs-deepagents-analysis-v3","provider":null,"model":null,"agent":null,"public_pack_mode":"local-pages-and-events","live_web_fetches":false,"note":"Public evidence exports do not trigger live Exa calls; stored Exa provenance is included when analysis metadata contains it."},"stats":{"pages":28,"events":73,"web":0,"evidence":88,"signal_desks":{"hiring":35,"forks":12,"releases":9,"talking":0,"repos":4},"data_radar_lanes":null,"data_radar_matches":null,"stored_analysis_evidence":null,"stored_analysis_web":null,"stored_analysis_signal_desks":null,"stored_analysis_data_radar_lanes":null,"stored_analysis_data_radar_matches":null},"stored_web_provenance":null,"evidence":[{"ref":"P1","kind":"page","title":"Member of Technical Staff","date":"2026-06-12T07:03:34.191132+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/fireworksai/jobs/4284300009","signal_url":null,"signal_json_url":null,"text":"Job Application for Member of Technical Staff at Fireworks AI \n\nBack to jobs New \nMember of Technical Staff\nNew York, NY\n\nApply \nAbout Us: \n\nAt Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI.\n\nJob Duties: Design, develop, and maintain large-scale backend and cloud-native infrastructure to\nsupport distributed machine learning training, inference, and data processing pipelines for generative AI platform.\nArchitect and build scalable, resilient backend infrastructure to support distributed training, inference, and data\nprocessing pipelines. Lead technical design discussions, mentor engineers, and establish best practices for\nlarge-scale machine learning systems. Design and implement core backend services with a focus on efficiency\nand low latency. Drive infrastructure optimization initiatives for compute cost, storage lifecycle management, and\nnetwork performance. Collaborate with machine learning, DevOps, and product teams to translate research and\nproduct requirements into robust infrastructure solutions. Evaluate and integrate cloud-native and open-source\ntechnologies such as Kubernetes, Ray, Kubeflow, and MLFlow to enhance platform reliability. Own end-to-end\nsystems from design to deployment, emphasizing reliability, fault tolerance, and operational excellence.\n\nMinimum Education & Experience Required : Bachelor’s degree or equivalent in Computer Science or related\nfield plus four (4) years of experience in software engineering or related role\n\nMinimum Skills Required: 4 years of experience designing, building, and optimizing large-scale backend\ninfrastructure and distributed data systems (e.g., Postg"},{"ref":"P2","kind":"page","title":"Paid Growth Marketer","date":"2026-06-12T07:03:33.934616+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/fireworksai/jobs/4283894009","signal_url":null,"signal_json_url":null,"text":"Job Application for Paid Growth Marketer at Fireworks AI \n\nBack to jobs New \nPaid Growth Marketer\nSan Mateo, CA\n\nApply \nAbout Us: \n\nAt Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI.\n\nAbout the Role \n\nFireworks is building one of the most important infrastructure platforms in AI. Just like frontier open-source inference enables sustainable unit economics for our customers, we are building a sustainable growth engine to drive demand gen for the Fireworks business. We're hiring a Paid Growth Marketer to lead all paid marketing programs across the full funnel, from awareness to engagement and activation.\n\nThis is a hands-on role. You'll manage multi-million dollar budgets with analytical rigor, build the measurement infrastructure that connects spend to pipeline, and develop Fireworks' internal capacity for structured paid experimentation. You'll also think strategically about where the paid landscape is heading and position us ahead of it.\n\nYou'll report into the Director of Growth and partner closely with Sales and Product to align paid programs with product launches, sales cycles, and ICP targeting. The ideal candidate will bring a strong growth mindset to everything they do, using AI as a productivity accelerant to move faster and operate more effectively than any traditional paid marketer could.\n\nResponsibilities \n\nOwn paid strategy and execution across paid search, social, display, OOH, and developer-focused channels\n\nManage and optimize a multi-million dollar ad budget, with full accountability for efficiency and attributable pipeline.\n\nBuild and maintain attribution and measurement frameworks that "},{"ref":"P3","kind":"page","title":"Director, Revenue Strategy & Analytics","date":"2026-06-12T07:03:33.863834+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/fireworksai/jobs/4280810009","signal_url":null,"signal_json_url":null,"text":"Job Application for Director, Revenue Strategy & Analytics at Fireworks AI \n\nBack to jobs New \nDirector, Revenue Strategy & Analytics\nRemote, USA; San Mateo, CA\n\nApply \nAbout Us: \n\nAt Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI.\n\nThe Role\n\nOur GTM organization is scaling fast and needs a strategic and analytical foundation to match. In this role you will lead our Sales Strategy and Revenue Analytics pillars, reporting to the VP of Revenue Operations. This role requires someone who has operated in a consumption or usage-based business and understands how that changes forecasting, territory design, and what good GTM metrics actually look like.\n\nYou'll own the foundation the GTM org runs on: operating model, territory and coverage design, revenue and GPU forecasting, QBR/WBR operating cadence, and the repeatable sales motions that turn annual strategy into quarterly execution. You'll be a strategic partner to sales leadership and a connector across Sales, Finance, Data, and Product.\n\nYou'll set the operating model, defend it with data, and change it when the business changes. We want someone who measures their impact in decisions changed, not decks delivered.\n\nWhat You'll Own\n\n1. GTM Operating Model and Territory/ROE Design\n\nOwn how the GTM organization is structured to win: segment coverage, territory design, rules of engagement across GTM and Product, and quota-setting philosophy. Translate annual targets into territory plans that give every rep a fair book and every segment the right level of coverage. Revisit and adjust as the business evolves.\n\n2. Revenue and GPU Forecasting\n\nBuild and own the fore"},{"ref":"P4","kind":"page","title":"AI Field Engineer - Microsoft Foundry","date":"2026-06-12T07:03:33.826203+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/fireworksai/jobs/4283962009","signal_url":null,"signal_json_url":null,"text":"Job Application for AI Field Engineer - Microsoft Foundry at Fireworks AI \n\nBack to jobs New \nAI Field Engineer - Microsoft Foundry\nSan Mateo, CA\n\nApply \nAbout Us: \n\nAt Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI.\n\nThe Role \n\nAs an AI Field Engineer for Microsoft Foundry, you will be one of the technical owners of Fireworks' most strategic partnership. You’ll work closely with Microsoft's field teams, Azure-aligned ISVs, and the SIs that run enterprise AI transformation programs to make Fireworks the default inference and fine-tuning layer in every Azure AI architecture your partners touch. The role sits at the intersection of engineering, partner development, and customer delivery. You build reference architectures, run benchmarks, debug production integrations, and co-develop POCs — all while holding your own in executive-level conversations about strategy, roadmap, and business outcomes.\n\nYou spend most of your time building and enabling. You ship code, run joint POCs with Microsoft field teams, and architect deployments that span Azure Foundry and Fireworks. But you also lead discovery conversations, align partner stakeholders, and translate field signals into product improvements that compress the feedback loop from partner to roadmap. \n\nThe Segment \n\nAs a Field Engineer aligned with our Partnerships team you own the technical relationship between Fireworks and the Microsoft ecosystem, Azure field teams, ISVs building on Azure Foundry, and the SIs that deliver AI transformation programs on Azure. The Microsoft partnership is a core go-to-market bet: clients like UIPath, Stack Blitz, Motif run vi"},{"ref":"P5","kind":"page","title":"AI Field Engineer - Enterprise","date":"2026-06-12T07:03:33.764259+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/fireworksai/jobs/4284317009","signal_url":null,"signal_json_url":null,"text":"Job Application for AI Field Engineer - Enterprise at Fireworks AI \n\nBack to jobs New \nAI Field Engineer - Enterprise\nNew York, NY; Remote, USA; San Mateo, CA\n\nApply \nAbout Us: \n\nAt Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI.\n\nIn the last few months alone we launched Fireworks Training, partnered with Microsoft Azure Foundry, and published research straight from our production systems. A few examples of what that looks like in practice:\n\nFrontier RL is cheaper than the mega-cluster narrative suggests: we ran cross-region rollouts using 98% sparse weight deltas and published what we learned. ( blog )\n\nOpen source agents with frontier advisors: matching frontier performance through training and harness engineering. ( blog )\n\nThe fine-tuning bottleneck is not the algorithm: integration friction and iteration speed are what actually stall teams; we documented the patterns across dozens of customer engagements. ( blog) \n\nThe Role \n\nAI Field Engineers at Fireworks are the technical tip of the spear. You embed with our most ambitious customers and technology partners to turn complex AI problems into production systems, fast. The role sits at the intersection of engineering, product, and customer delivery. You are hands-on-keyboard building POCs, MVPs, and production integrations, while also holding your own in executive-level conversations about architecture, strategy, and business outcomes.\n\nYou spend most of your time building. You ship code, run benchmarks, debug production issues, and architect deployments. But you also lead discovery conversations, align stakeholders, and translate customer pain point"},{"ref":"P6","kind":"page","title":"fw-ai/cookbook cookbook-v2026.06.11.1","date":"2026-06-11T07:04:03.253531+00:00","date_source":null,"source_url":"https://github.com/fw-ai/cookbook/releases/tag/cookbook-v2026.06.11.1","signal_url":null,"signal_json_url":null,"text":"# cookbook-v2026.06.11.1\n\nRepository: fw-ai/cookbook\n\nTag: cookbook-v2026.06.11.1\n\nPublished: 2026-06-11T04:49:36Z\n\nPrerelease: no\n\nRelease notes:\n## What's Changed\n* [Promote] From Fireworks staging to cookbook by @bot-fireworks-ai in https://github.com/fw-ai/cookbook/pull/506\n\n**Full Changelog**: https://github.com/fw-ai/cookbook/compare/cookbook-v2026.06.09.2...cookbook-v2026.06.11.1"},{"ref":"P7","kind":"page","title":"fw-ai/kserve repository metadata","date":"2026-06-11T04:19:35.427476+00:00","date_source":null,"source_url":"https://github.com/fw-ai/kserve","signal_url":null,"signal_json_url":null,"text":"# fw-ai/kserve\n\nDescription: Standardized Serverless ML Inference Platform on Kubernetes\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 0\n\nOpen issues: 5\n\nCreated: 2023-05-18T20:41:14Z\n\nPushed: 2026-05-20T04:25:47Z\n\nDefault branch: master\n\nFork: yes\n\nParent repository: kserve/kserve\n\nArchived: no\n\nREADME:\n# KServe\n[![go.dev reference](https://img.shields.io/badge/go.dev-reference-007d9c?logo=go&logoColor=white)](https://pkg.go.dev/github.com/kserve/kserve)\n[![Coverage Status](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/andyi2it/5174bd748ac63a6e4803afea902e9810/raw/coverage.json)](https://github.com/kserve/kserve/actions/workflows/go.yml)\n[![Go Report Card](https://goreportcard.com/badge/github.com/kserve/kserve)](https://goreportcard.com/report/github.com/kserve/kserve)\n[![OpenSSF Best Practices](https://bestpractices.coreinfrastructure.org/projects/6643/badge)](https://bestpractices.coreinfrastructure.org/projects/6643)\n[![Releases](https://img.shields.io/github/release-pre/kserve/kserve.svg?sort=semver)](https://github.com/kserve/kserve/releases)\n[![LICENSE](https://img.shields.io/github/license/kserve/kserve.svg)](https://github.com/kserve/kserve/blob/master/LICENSE)\n[![Slack Status](https://img.shields.io/badge/slack-join_chat-white.svg?logo=slack&style=social)](https://kubeflow.slack.com/archives/CH6E58LNP)\n\nKServe provides a Kubernetes [Custom Resource Definition](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) for serving machine learning (ML) models on arbitrary frameworks. It aims to solve production model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX.\n\nIt encapsulates the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU Autoscaling, Scale to Zero, and Canary Rollouts to your ML deployments. It enables a simple, pluggable, and complete story for Production ML Serving including prediction, pre-processing, post-processing and explainability. KServe is being [used across various organizations.](htt"},{"ref":"P8","kind":"page","title":"fw-ai/go-helm-client repository metadata","date":"2026-06-11T04:19:35.213738+00:00","date_source":null,"source_url":"https://github.com/fw-ai/go-helm-client","signal_url":null,"signal_json_url":null,"text":"# fw-ai/go-helm-client\n\nDescription: Go client for accessing the Helm package manager\n\nLanguage: Go\n\nLicense: MIT\n\nStars: 0\n\nForks: 0\n\nOpen issues: 6\n\nCreated: 2023-06-13T23:26:48Z\n\nPushed: 2026-05-20T04:17:14Z\n\nDefault branch: master\n\nFork: yes\n\nParent repository: mittwald/go-helm-client\n\nArchived: no\n\nREADME:\n# Go Helm Client\nGo client library for accessing [Helm](https://github.com/helm/helm), enabling the user to programmatically change helm charts and releases.\n\nThis library is build upon [`helm`](https://github.com/helm/helm) and available under the MIT License.\n\n![Compile & Test](https://github.com/mittwald/go-helm-client/workflows/Compile%20&%20Test/badge.svg)\n[![GitHub license](https://img.shields.io/github/license/mittwald/go-helm-client.svg)](https://github.com/mittwald/go-helm-client/blob/master/LICENSE)\n[![Go Report Card](https://goreportcard.com/badge/github.com/mittwald/go-helm-client)](https://goreportcard.com/report/github.com/mittwald/go-helm-client)\n[![Documentation](https://godoc.org/github.com/mittwald/go-helm-client?status.svg)](https://pkg.go.dev/github.com/mittwald/go-helm-client)\n\n## Installation\nInstall this library using `go get`:\n\n$ go get github.com/mittwald/go-helm-client\n\n## Usage\nExample usage of the client can be found in the [package examples](https://pkg.go.dev/github.com/mittwald/go-helm-client?tab=doc#pkg-examples).\n\n#### Private chart repository\nWhen working with private repositories, you can utilize the `Username` and `Password` parameters of a chart entry to specify credentials.\n\nAn example of this can be found in the corresponding [example](https://pkg.go.dev/github.com/mittwald/go-helm-client?tab=doc#example_HelmClient_AddOrUpdateChartRepo_private).\n\n## Mock Client\nThis library includes a mock client [mock/interface_mock.go](mock/interface.go) which is generated by [mockgen](https://github.com/golang/mock).\n\nExample usage of the mocked client can be found in [mock/mock_test.go](mock/mock_test.go).\n\nIf you made changes to [interface.go](./interface.go), you should issue the `make generate` command to trigger code generation.\n\n## Documentation\nFor more specific documentation, please refer to the [godoc](https://pkg.go.dev/"},{"ref":"P9","kind":"page","title":"fw-ai/postgres repository metadata","date":"2026-06-11T04:19:34.790537+00:00","date_source":null,"source_url":"https://github.com/fw-ai/postgres","signal_url":null,"signal_json_url":null,"text":"# fw-ai/postgres\n\nDescription: GORM PostgreSQL driver\n\nLanguage: Go\n\nLicense: MIT\n\nStars: 0\n\nForks: 0\n\nOpen issues: 1\n\nCreated: 2023-06-23T23:24:44Z\n\nPushed: 2026-05-20T04:15:40Z\n\nDefault branch: master\n\nFork: yes\n\nParent repository: go-gorm/postgres\n\nArchived: no\n\nREADME:\n# GORM PostgreSQL Driver\n\n## Quick Start\n\n```go\nimport (\n\"gorm.io/driver/postgres\"\n\"gorm.io/gorm\"\n)\n\n// https://github.com/jackc/pgx\ndsn := \"host=localhost user=gorm password=gorm dbname=gorm port=9920 sslmode=disable TimeZone=Asia/Shanghai\"\ndb, err := gorm.Open(postgres.Open(dsn), &gorm.Config{})\n```\n\n## Configuration\n\n```go\nimport (\n\"gorm.io/driver/postgres\"\n\"gorm.io/gorm\"\n)\n\ndb, err := gorm.Open(postgres.New(postgres.Config{\nDSN: \"host=localhost user=gorm password=gorm dbname=gorm port=9920 sslmode=disable TimeZone=Asia/Shanghai\", // data source name, refer https://github.com/jackc/pgx\nPreferSimpleProtocol: true, // disables implicit prepared statement usage. By default pgx automatically uses the extended protocol\n}), &gorm.Config{})\n```\n\nCheckout [https://gorm.io](https://gorm.io) for details."},{"ref":"P10","kind":"page","title":"fw-ai/langchain repository metadata","date":"2026-06-11T04:19:34.664616+00:00","date_source":null,"source_url":"https://github.com/fw-ai/langchain","signal_url":null,"signal_json_url":null,"text":"# fw-ai/langchain\n\nDescription: ⚡ Building applications with LLMs through composability ⚡\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 3\n\nForks: 0\n\nOpen issues: 9\n\nCreated: 2023-07-26T20:06:56Z\n\nPushed: 2026-06-10T21:31:21Z\n\nDefault branch: master\n\nFork: yes\n\nParent repository: langchain-ai/langchain\n\nArchived: no\n\nREADME:\n# 🦜️🔗 LangChain\n\n⚡ Building applications with LLMs through composability ⚡\n\n[![Release Notes](https://img.shields.io/github/release/langchain-ai/langchain)](https://github.com/langchain-ai/langchain/releases)\n[![CI](https://github.com/langchain-ai/langchain/actions/workflows/langchain_ci.yml/badge.svg)](https://github.com/langchain-ai/langchain/actions/workflows/langchain_ci.yml)\n[![Experimental CI](https://github.com/langchain-ai/langchain/actions/workflows/langchain_experimental_ci.yml/badge.svg)](https://github.com/langchain-ai/langchain/actions/workflows/langchain_experimental_ci.yml)\n[![Downloads](https://static.pepy.tech/badge/langchain/month)](https://pepy.tech/project/langchain)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)\n[![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.gg/6adMQxSpJS)\n[![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)\n[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/langchain-ai/langchain)\n[![GitHub star chart](https://img.shields.io/github/stars/langchain-ai/langchain?style=social)](https://star-history.com/#langchain-ai/langchain)\n[![Dependency Status](https://img.shields.io/librariesio/github/langchain-ai/langchain)](https://libraries.io/github/langchain-ai/langchain)\n[![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langchain)](https://github.com/langchain-ai/langchain/issues"},{"ref":"P11","kind":"page","title":"fw-ai/autogen repository metadata","date":"2026-06-11T04:19:34.492926+00:00","date_source":null,"source_url":"https://github.com/fw-ai/autogen","signal_url":null,"signal_json_url":null,"text":"# fw-ai/autogen\n\nDescription: Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ\n\nLanguage: Jupyter Notebook\n\nLicense: CC-BY-4.0\n\nStars: 1\n\nForks: 0\n\nOpen issues: 9\n\nCreated: 2024-01-17T07:00:53Z\n\nPushed: 2026-06-09T20:00:39Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: microsoft/autogen\n\nArchived: no\n\nREADME:\n[![PyPI version](https://badge.fury.io/py/pyautogen.svg)](https://badge.fury.io/py/pyautogen)\n[![Build](https://github.com/microsoft/autogen/actions/workflows/python-package.yml/badge.svg)](https://github.com/microsoft/autogen/actions/workflows/python-package.yml)\n![Python Version](https://img.shields.io/badge/3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue)\n[![Downloads](https://static.pepy.tech/badge/pyautogen/week)](https://pepy.tech/project/pyautogen)\n[![](https://img.shields.io/discord/1153072414184452236?logo=discord&style=flat)](https://discord.gg/pAbnFJrkgZ)\n[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/cloudposse.svg?style=social&label=Follow%20%40pyautogen)](https://twitter.com/pyautogen)\n\n# AutoGen\n[📚 Cite paper](#related-papers).\n<!-- <p align=\"center\">\n<img src=\"https://github.com/microsoft/autogen/blob/main/website/static/img/flaml.svg\" width=200>\n<br>\n</p> -->\n\n:fire: Dec 31: [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework](https://arxiv.org/abs/2308.08155) is selected by [TheSequence: My Five Favorite AI Papers of 2023](https://thesequence.substack.com/p/my-five-favorite-ai-papers-of-2023).\n\n<!-- :fire: Nov 24: pyautogen [v0.2](https://github.com/microsoft/autogen/releases/tag/v0.2.0) is released with many updates and new features compared to v0.1.1. It switches to using openai-python v1. Please read the [migration guide](https://microsoft.github.io/autogen/docs/Installation#python). -->\n\n<!-- :fire: Nov 11: OpenAI's Assistants are available in AutoGen and interoperatable with other AutoGen agents! Checkout our [blogpost](https://microsoft.github.io/autogen/blog/2023/11/13/OAI-assistants) for details and examples. -->\n\n:fire: Nov 8: AutoGen is selected into [Open100: Top 100 Open Source achievements](https://www.benchcouncil.org/ev"},{"ref":"P12","kind":"page","title":"fw-ai/llama-cuda-graph-example repository metadata","date":"2026-06-11T04:19:34.484772+00:00","date_source":null,"source_url":"https://github.com/fw-ai/llama-cuda-graph-example","signal_url":null,"signal_json_url":null,"text":"# fw-ai/llama-cuda-graph-example\n\nDescription: Example of applying CUDA graphs to LLaMA-v2\n\nLicense: NOASSERTION\n\nStars: 11\n\nForks: 5\n\nOpen issues: 0\n\nCreated: 2023-08-16T21:36:02Z\n\nPushed: 2023-08-25T23:08:38Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: meta-llama/llama\n\nArchived: no\n\nREADME:\n# Llama 2\n\nWe are unlocking the power of large language models. Our latest version of Llama is now accessible to individuals, creators, researchers and businesses of all sizes so that they can experiment, innovate and scale their ideas responsibly. \n\nThis release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters.\n\nThis repository is intended as a minimal example to load [Llama 2](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/) models and run inference. For more detailed examples leveraging HuggingFace, see [llama-recipes](https://github.com/facebookresearch/llama-recipes/).\n\n## Updates post-launch\n\nSee [UPDATES.md](UPDATES.md).\n\n## Download\n\n⚠️ **7/18: We're aware of people encountering a number of download issues today. Anyone still encountering issues should remove all local files, re-clone the repository, and [request a new download link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/). It's critical to do all of these in case you have local corrupt files. When you receive the email, copy *only* the link text - it should begin with https://download.llamameta.net and not with https://l.facebook.com, which will give errors.**\n\nIn order to download the model weights and tokenizer, please visit the [Meta AI website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License.\n\nOnce your request is approved, you will receive a signed URL over email. Then run the download.sh script, passing the URL provided when prompted to start the download. Make sure that you copy the URL text itself, **do not use the 'Copy link address' option** when you right click the URL. If the copied URL text starts with: https://download.llamameta.net, you copied it correctly. If the copied URL text starts with: ht"},{"ref":"P13","kind":"page","title":"fw-ai/fireworks_poe_image_bot repository metadata","date":"2026-06-11T04:19:34.253312+00:00","date_source":null,"source_url":"https://github.com/fw-ai/fireworks_poe_image_bot","signal_url":null,"signal_json_url":null,"text":"# fw-ai/fireworks_poe_image_bot\n\nDescription: Infrastructure to run a poe.com bot based on Fireworks.ai hosted image generation models\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 1\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2023-11-04T20:27:06Z\n\nPushed: 2023-11-27T21:20:42Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: fw-ai/fireworks_poe_bot\n\nArchived: yes\n\nREADME:\n# DEPRECATED: Please use https://github.com/fw-ai/fireworks_poe_bot\n\n# Fireworks Poe Image Generation Bot\n\nThis codebase provides an implementation of a Poe server bot that calls into image generation models on [Fireworks](fireworks.ai).\n\n## Server Arguments\n\nThe server has several important arguments:\n\n* `--model` - This flag specifies the model to call into on Fireworks. For example: `accounts/fireworks/models/stable-diffusion-xl-1024-v1-0`\n* `FIREWORKS_API_KEY` - This environment variable specifies the API key to use to call into Fireworks. API keys can be retrieved from the [Fireworks console](https://app.fireworks.ai/users?tab=apps).\n\nThere are several optional arguments that can usually be left as default:\n\n* `--host` specifies the hostname to bind to. You usually don't need to set this\n* `--port` specifies the port for the bot to listen on. By default this is port 80.\n* `FIREWORKS_API_BASE` environment variable specifies the base URL to call into Fireworks. This is `https://api.fireworks.ai` by default. You usually don't need to modify this.\n\n## Running the bot\n\nThe bot can be run locally by installing the package and running the module:\n\n```bash\n$ pip install -e .\n$ FIREWORKS_API_KEY=<your API key> python -m fireworks_poe_image_bot --model accounts/fireworks/models/stable-diffusion-xl-1024-v1-0\nINFO: Started server process [50060]\nINFO: Waiting for application startup.\nINFO: Application startup complete.\nINFO: Uvicorn running on http://0.0.0.0:80 (Press CTRL+C to quit)\n```\n\nThe server will then be listening on port 80. You can then communicate with the bot using the [Poe simulator](https://github.com/poe-platform/poe-protocol/tree/main/simulator_poe) for testing:\n\n```bash\n# BOT_SERVER=localhost:80 python -m simulator_poe\nWelcome to the Poe server simulator!\n!q -- quit Poe server simulator\n"},{"ref":"P14","kind":"page","title":"fw-ai/flash-attention repository metadata","date":"2026-06-11T04:10:33.180753+00:00","date_source":null,"source_url":"https://github.com/fw-ai/flash-attention","signal_url":null,"signal_json_url":null,"text":"# fw-ai/flash-attention\n\nDescription: Clone of https://github.com/HazyResearch/flash-attention/\n\nLanguage: Python\n\nLicense: BSD-3-Clause\n\nStars: 0\n\nForks: 0\n\nOpen issues: 1\n\nCreated: 2023-06-28T20:17:47Z\n\nPushed: 2026-04-04T03:39:06Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# FlashAttention\nThis repository provides the official implementation of FlashAttention and\nFlashAttention-2 from the\nfollowing papers.\n\n**FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness** \nTri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré \nPaper: https://arxiv.org/abs/2205.14135 \nIEEE Spectrum [article](https://spectrum.ieee.org/mlperf-rankings-2022) about our submission to the MLPerf 2.0 benchmark using FlashAttention.\n![FlashAttention](assets/flashattn_banner.jpg)\n\n**FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning** \nTri Dao\n\nPaper: https://tridao.me/publications/flash2/flash2.pdf\n\n![FlashAttention-2](assets/flashattention_logo.png)\n\n## Usage\n\nWe've been very happy to see FlashAttention being widely adopted in such a short\ntime after its release. This [page](https://github.com/Dao-AILab/flash-attention/blob/main/usage.md)\ncontains a partial list of places where FlashAttention is being used.\n\nFlashAttention and FlashAttention-2 are free to use and modify (see LICENSE).\nPlease cite and credit FlashAttention if you use it.\n\n## Installation and features\n\nRequirements:\n- CUDA 11.6 and above.\n- PyTorch 1.12 and above.\n- Linux. Might work for Windows starting v2.3.2 (we've seen a few positive [reports](https://github.com/Dao-AILab/flash-attention/issues/595)) but Windows compilation still requires more testing. If you have ideas on how to set up prebuilt CUDA wheels for Windows, please reach out via Github issue.\n\nWe recommend the\n[Pytorch](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch)\ncontainer from Nvidia, which has all the required tools to install FlashAttention.\n\nTo install:\n1. Make sure that PyTorch is installed.\n2. Make sure that `packaging` is installed (`pip install packaging`)\n3. Make sure that `ninja` is installed and that it works correctly (e.g. `ninja\n--version` then `echo $?` "},{"ref":"P15","kind":"page","title":"fw-ai/benchmark repository metadata","date":"2026-06-11T04:10:32.629409+00:00","date_source":null,"source_url":"https://github.com/fw-ai/benchmark","signal_url":null,"signal_json_url":null,"text":"# fw-ai/benchmark\n\nDescription: Benchmark suite for LLMs from Fireworks.ai\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 105\n\nForks: 39\n\nOpen issues: 21\n\nCreated: 2023-11-03T18:00:04Z\n\nPushed: 2026-06-06T19:04:33Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Benchmark / Load-testing Suite by Fireworks.ai\n\n## LLM benchmarking\n\nThe load test is designed to simulate continuous production load and minimize effect of model generation behavior:\n* variation in generation parameters\n* continuous request stream with varying distribution and load levels\n* force generation of exact number of output tokens (for most providers)\n* specified load test duration\n\nSupported providers and API flavors:\n* OpenAI API compatible endpoints:\n* [Fireworks.ai](https://app.fireworks.ai) public or private deployments\n* VLLM\n* Anyscale Endpoints\n* OpenAI\n* Text Generation Inference (TGI) / HuggingFace Endpoints\n* Together.ai\n* NVidia Triton server:\n* Legacy HTTP endpoints (no streaming)\n* LLM-focused endpoints (with or without streaming)\n\nSupported API types:\n* Chat completions (`/v1/chat/completions`)\n* Text completions (`/v1/completions`)\n* Embeddings (`/v1/embeddings`)\n* Rerank (`/v1/rerank`)\n\nCaptured metrics:\n* Overall latency\n* Number of generated tokens\n* Sustained requests throughput (QPS)\n* Time to first token (TTFT) for streaming\n* Per token latency for streaming\n\nMetrics summary can be exported to CSV. This way multiple configuration can be scripted over. CSV file can be imported to Google Sheets/Excel or Jupyter for further analysis.\n\n## Local Setup\n\nThe fastest way to get started is with [uv](https://github.com/astral-sh/uv):\n\n```bash\nbash scripts/setup.sh\n```\n\nThis will install `uv` (if needed), create a `.venv` with Python 3.11, and install all dependencies.\n\nThen activate the environment:\n\n```bash\nsource .venv/bin/activate\n```\n\n## Usage\n\nSee [`llm_bench`](llm_bench) folder for detailed usage.\n\nSee [`llm_bench/benchmark_suite.ipynb`](llm_bench/benchmark_suite.ipynb) for a detailed example of how to use the load test script and run different types of benchmark suites."},{"ref":"P16","kind":"page","title":"fw-ai/homebrew-firectl repository metadata","date":"2026-06-11T04:10:32.38881+00:00","date_source":null,"source_url":"https://github.com/fw-ai/homebrew-firectl","signal_url":null,"signal_json_url":null,"text":"# fw-ai/homebrew-firectl\n\nLanguage: Ruby\n\nStars: 0\n\nForks: 1\n\nOpen issues: 3\n\nCreated: 2024-05-23T23:56:15Z\n\nPushed: 2026-06-05T19:08:30Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Fireworks Firectl\n\n## How do I install these formulae?\n\n```bash\nbrew tap fw-ai/firectl\nbrew install firectl\n```\n\nOr, in a [`brew bundle`](https://github.com/Homebrew/homebrew-bundle) `Brewfile`:\n\n```ruby\ntap \"fw-ai/firectl\"\nbrew \"<formula>\"\n```\n\n## Documentation\n\n`brew help`, `man brew` or check [Homebrew's documentation](https://docs.brew.sh).\n\n## Trigger\n\nThe workflow to update the SHA256 hash is triggered by a firectl stable release: https://github.com/fw-ai/fireworks/blob/719924c123401ce01e569a04e0138973cfead0ec/.github/workflows/firectl_release.yml#L63-L70"},{"ref":"P17","kind":"page","title":"fw-ai/forge repository metadata","date":"2026-06-11T04:10:32.364811+00:00","date_source":null,"source_url":"https://github.com/fw-ai/forge","signal_url":null,"signal_json_url":null,"text":"# fw-ai/forge\n\nLanguage: TypeScript\n\nStars: 17\n\nForks: 7\n\nOpen issues: 9\n\nCreated: 2024-02-05T07:14:34Z\n\nPushed: 2026-05-30T09:09:29Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Fireworks Demo Apps\n\nThis repository contains demo apps illustrating the capabilities of large language and image models. Individual apps are self-contained and easy to expand.\n\nHave fun!\n\n## List of apps ( :construction: more apps coming soon)\n\n- [Functional chat](https://github.com/fw-ai/forge/tree/main/apps/functional_chat) - an LLM powered chat with function calling capabilities.\n\n## External apps leveraging Fireworks models\n\n- [VexaSearch](https://github.com/n4ze3m/vexasearch/tree/main) - a simple AI-powered search application designed to determine the actions to perform based on a function call."},{"ref":"P18","kind":"page","title":"fw-ai/fireworks_poe_bot repository metadata","date":"2026-06-11T04:10:32.335066+00:00","date_source":null,"source_url":"https://github.com/fw-ai/fireworks_poe_bot","signal_url":null,"signal_json_url":null,"text":"# fw-ai/fireworks_poe_bot\n\nDescription: Infrastructure to run a poe.com bot based on Fireworks.ai hosted models\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 9\n\nForks: 6\n\nOpen issues: 3\n\nCreated: 2023-11-03T23:24:23Z\n\nPushed: 2025-08-09T16:15:09Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Fireworks Poe Bot\n\nThis codebase is the underlying implementation of the [Mistral-7B](https://poe.com/fw-mistral-7b) on Poe. This implementation calls into the `accounts/fireworks/models/mistral-7b-instruct-4k` model from [fireworks.ai](https://app.fireworks.ai/). However, this bot implementation is fully general and can be used to create bots that call into other Fireworks models.\n\n## Server Arguments\n\nThe server has several important arguments:\n\n* `FIREWORKS_API_KEY` - This environment variable specifies the API key to use to call into Fireworks. API keys can be retrieved from the [Fireworks console](https://app.fireworks.ai/users?tab=apps).\n\nThere are several optional arguments that can usually be left as default:\n\n* `--host` specifies the hostname to bind to. You usually don't need to set this\n* `--port` specifies the port for the bot to listen on. By default this is port 80.\n* `FIREWORKS_API_BASE` environment variable specifies the base URL to call into Fireworks. This is `https://api.fireworks.ai` by default. You usually don't need to modify this.\n\nYou can read more from `fireworks_poe_bot/__main__.py` for more details on arguments.\n\n## Running the bot\n\nThe bot can be run locally by installing the package and running the module:\n\n```bash\n$ pip install -e .\n# Ensure that you have the config.json containing bot configurations ready to use.\n$ FIREWORKS_API_KEY=<your API key> python -m fireworks_poe_bot\ntruct-4k\nINFO: Started server process [50060]\nINFO: Waiting for application startup.\nINFO: Application startup complete.\nINFO: Uvicorn running on http://0.0.0.0:80 (Press CTRL+C to quit)\n```\n\nThe server will then be listening on port 80. You can then communicate with the bot using the [Poe simulator](https://github.com/poe-platform/poe-protocol/tree/main/simulator_poe) for testing:\n\n```bash\n# BOT_SERVER=localhost:80 python -m simulator_poe\nWelcome to the Poe ser"},{"ref":"P19","kind":"page","title":"fw-ai/cookbook repository metadata","date":"2026-06-11T04:10:32.29326+00:00","date_source":null,"source_url":"https://github.com/fw-ai/cookbook","signal_url":null,"signal_json_url":null,"text":"# fw-ai/cookbook\n\nDescription: Recipes and resources for building, deploying, and fine-tuning generative AI with Fireworks.\n\nLanguage: Jupyter Notebook\n\nLicense: Apache-2.0\n\nStars: 161\n\nForks: 48\n\nOpen issues: 8\n\nCreated: 2023-08-12T19:20:11Z\n\nPushed: 2026-06-11T00:05:04Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Fireworks AI Cookbook\n\nReady-to-run training recipes for reinforcement learning (GRPO, DAPO, GSPO, CISPO), preference optimization (DPO, ORPO), and supervised fine-tuning (SFT) on [Fireworks](https://fireworks.ai).\n\n> **Full documentation**: [Fireworks Training SDK Reference](https://docs.fireworks.ai/fine-tuning/training-sdk/introduction)\n\n## Quick Start\n\n```bash\ngit clone https://github.com/fw-ai/cookbook.git\ncd cookbook/training\nconda create -n cookbook python=3.12 -y && conda activate cookbook\npip install --pre -e .\n```\n\nSee [`training/README.md`](./training/README.md) for configuration, recipes, and examples.\n\n## For AI Agents\n\nThe primary reference for agents working in this repo is **[`skills/dev/SKILL.md`](skills/dev/SKILL.md)** — it maps tasks and error signals to specific reference files. Start there, not the READMEs.\n\n## Repository Structure\n\nOnly `training/` is actively developed. Other top-level directories (`integrations/`, `multimedia/`, `archived/`) are kept for backward compatibility.\n\n```\ntraining/ Training SDK recipes, utilities, and examples\nrecipes/ Fork-and-customize training loop scripts\nutils/ Shared config, data loading, losses, metrics\nexamples/ Worked examples (RL, SFT, DPO, ORPO)\nverifier/ Renderer correctness validator + live React viewer\ntests/ Unit and end-to-end tests\nskills/ Agent skills and reference docs\n```\n\n## Fireworks Agent skill\n\n- [`skills/fireworks-agent/SKILL.md`](skills/fireworks-agent/SKILL.md)\n— end-to-end fine-tuning via the Fireworks Agent (`firectl\nsession`). Give it one natural-language instruction and it handles\ndata inspection, model selection, hyperparameter sweeps, training,\nevaluation, and deployment. Includes the full session lifecycle:\ncreate, stream events, answer the agent's mid-run questions,\nrecover from failures, and clean up.\n\n## Contributing\n\nSee the [Contribution Guide](./Co"},{"ref":"P20","kind":"page","title":"fw-ai/llm_eval_meta repository metadata","date":"2026-06-11T04:10:14.139229+00:00","date_source":null,"source_url":"https://github.com/fw-ai/llm_eval_meta","signal_url":null,"signal_json_url":null,"text":"# fw-ai/llm_eval_meta\n\nDescription: Repro for official Llama 3.1 Benchmarks\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 2\n\nForks: 0\n\nOpen issues: 1\n\nCreated: 2024-08-03T20:48:40Z\n\nPushed: 2025-04-08T22:55:55Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Example script to run\n\n```\nOPENAI_API_KEY=<FW_API_KEY/TG_API_KEY> python run_meta_benchmarks.py --model-size 8b --provider fw --output-dir gsm8k/fw_3p1_8b/ --eval-set evals__gsm8k__details\n\n# Note - if this crashes due to rate limit/something else, you can rerun the same command to continue - all the previous requests are persisted\n\npython analyze_answers.py --task evals__gsm8k__details --response-path gsm8k/fw_3p1_8b/\n\n> Accuracy: 0.8529188779378317 evals__gsm8k__details gsm8k/fw_3p1_8b/\n```\n\nTasks supported so far are evals__mmlu__details, evals__mmlu__0_shot__cot__details, evals__gsm8k__details, evals__mmlu_pro__details.\n\nNote - we don't know the exact answer extraction logic Meta uses so we rolled out own. Discrepencies may be a result of this."},{"ref":"P21","kind":"page","title":"fw-ai/ai-starter-kits repository metadata","date":"2026-06-11T04:10:14.001807+00:00","date_source":null,"source_url":"https://github.com/fw-ai/ai-starter-kits","signal_url":null,"signal_json_url":null,"text":"# fw-ai/ai-starter-kits\n\nDescription: AI App Starter Templates - ChatBots, Summarisers and more!\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 6\n\nForks: 4\n\nOpen issues: 1\n\nCreated: 2024-08-12T15:06:41Z\n\nPushed: 2026-05-29T20:14:55Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# ai-starter-kits\n\nAI App Starter Templates - ChatBots, Summarisers and more!\n\n### Retrieval Augmented Generation\n\n* [Building a RAG with Astro, FastAPI, SurrealDB and Llama 3.1](rag/with_surrealdb)"},{"ref":"P22","kind":"page","title":"fw-ai/.github repository metadata","date":"2026-06-11T04:10:13.991386+00:00","date_source":null,"source_url":"https://github.com/fw-ai/.github","signal_url":null,"signal_json_url":null,"text":"# fw-ai/.github\n\nStars: 0\n\nForks: 0\n\nOpen issues: 2\n\nCreated: 2024-06-11T22:31:07Z\n\nPushed: 2026-04-28T18:19:42Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME: none published or not readable through the GitHub API."},{"ref":"P23","kind":"page","title":"fw-ai/llm_eval repository metadata","date":"2026-06-11T04:10:13.714288+00:00","date_source":null,"source_url":"https://github.com/fw-ai/llm_eval","signal_url":null,"signal_json_url":null,"text":"# fw-ai/llm_eval\n\nDescription: Fireworks OSS Evaluation suite for evals\n\nLanguage: Python\n\nStars: 2\n\nForks: 0\n\nOpen issues: 1\n\nCreated: 2024-12-09T19:41:29Z\n\nPushed: 2025-06-29T06:44:32Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# LLM Evaluation Suite by Fireworks.ai\n\n## Installation\n\n```bash\npip install -e .\npip install -r requirements.txt\n```\n\n## Usage\n\nSee [`llm_eval`](llm_eval) folder for detailed usage."},{"ref":"P24","kind":"page","title":"fw-ai/flashinfer repository metadata","date":"2026-06-11T04:10:13.62826+00:00","date_source":null,"source_url":"https://github.com/fw-ai/flashinfer","signal_url":null,"signal_json_url":null,"text":"# fw-ai/flashinfer\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 2\n\nForks: 1\n\nOpen issues: 22\n\nCreated: 2025-10-21T05:11:02Z\n\nPushed: 2026-05-15T21:25:03Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n<p align=\"center\">\n<picture>\n<source media=\"(prefers-color-scheme: dark)\" srcset=\"https://github.com/flashinfer-ai/web-data/blob/main/logo/FlashInfer-black-background.png?raw=true\">\n<img alt=\"FlashInfer\" src=\"https://github.com/flashinfer-ai/web-data/blob/main/logo/FlashInfer-white-background.png?raw=true\" width=55%>\n</picture>\n</p>\n<h1 align=\"center\">\nHigh-Performance GPU Kernels for Inference\n</h1>\n\n<p align=\"center\">\n| <a href=\"https://docs.flashinfer.ai\"><b>Documentation</b></a> | <a href=\"https://github.com/flashinfer-ai/flashinfer/releases/latest\"><b>Latest Release</b></a> | <a href=\"https://flashinfer.ai\"><b>Blog</b></a> | <a href=\"https://join.slack.com/t/flashinfer/shared_invite/zt-379wct3hc-D5jR~1ZKQcU00WHsXhgvtA\"><b>Slack</b></a> | <a href=\"https://github.com/orgs/flashinfer-ai/discussions\"><b>Discussion Forum</b></a> |\n</p>\n\n[![Build Status](https://ci.tlcpack.ai/job/flashinfer-ci/job/main/badge/icon)](https://ci.tlcpack.ai/job/flashinfer-ci/job/main/)\n[![Documentation](https://github.com/flashinfer-ai/flashinfer/actions/workflows/build-doc.yml/badge.svg)](https://github.com/flashinfer-ai/flashinfer/actions/workflows/build-doc.yml)\n\n**FlashInfer** is a library and kernel generator for inference that delivers state-of-the-art performance across diverse GPU architectures. It provides unified APIs for attention, GEMM, and MoE operations with multiple backend implementations including FlashAttention-2/3, cuDNN, CUTLASS, and TensorRT-LLM.\n\n## Why FlashInfer?\n\n- **State-of-the-art Performance**: Optimized kernels for prefill, decode, and mixed batching scenarios\n- **Multiple Backends**: Automatically selects the best backend for your hardware and workload\n- **Modern Architecture Support**: Support for SM75 (Turing) and later (through Blackwell)\n- **Low-Precision Compute**: FP8 and FP4 quantization for attention, GEMM, and MoE operations\n- **Production-Ready**: CUDAGraph and torch.compile compatible for low-latency serving\n\n## Core Features\n\n### Atte"},{"ref":"P25","kind":"page","title":"fw-ai/k8s-stackdriver repository metadata","date":"2026-06-11T03:02:07.640832+00:00","date_source":null,"source_url":"https://github.com/fw-ai/k8s-stackdriver","signal_url":null,"signal_json_url":null,"text":"# fw-ai/k8s-stackdriver\n\nDescription: Clone of https://github.com/GoogleCloudPlatform/k8s-stackdriver\n\nLanguage: Go\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 0\n\nOpen issues: 4\n\nCreated: 2024-07-02T01:18:41Z\n\nPushed: 2026-05-20T04:17:13Z\n\nDefault branch: master\n\nFork: yes\n\nParent repository: GoogleCloudPlatform/k8s-stackdriver\n\nArchived: no\n\nREADME:\n## Google Cloud Operations integration for GKE\n\n> :exclamation: **Tools in this repository are not meant for \n> end-users.**\n> It contains source code for tools that are pre-installed in \n> the GKE clusters.\n\n[Google Cloud Operations suite][cloudOperationsSite] (fka Stackdriver) provides advanced \nmonitoring and logging solution that will allow you to get more\ninsights into your Kubernetes clusters. If you are a Google\nKubernetes Engine (GKE) user, you get [integration][k8sMonitoring]\nwith Cloud Monitoring and Logging out of the box.\n\n[k8sMonitoring]: https://cloud.google.com/kubernetes-engine-monitoring\n[cloudOperationsSite]: https://cloud.google.com/products/operations"},{"ref":"P26","kind":"page","title":"fw-ai/helm repository metadata","date":"2026-06-11T03:02:05.979888+00:00","date_source":null,"source_url":"https://github.com/fw-ai/helm","signal_url":null,"signal_json_url":null,"text":"# fw-ai/helm\n\nDescription: Holistic Evaluation of Language Models (HELM), a framework to increase the transparency of language models (https://arxiv.org/abs/2211.09110). This framework is also used to evaluate text-to-image models in Holistic Evaluation of Text-to-Image Models (HEIM) (https://arxiv.org/abs/2311.04287).\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 0\n\nOpen issues: 13\n\nCreated: 2024-07-23T18:38:00Z\n\nPushed: 2026-06-08T17:37:09Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: stanford-crfm/helm\n\nArchived: no\n\nREADME:\n<!--intro-start-->\n\n# Holistic Evaluation of Language Models\n\n[comment]: <> (When using the img tag, which allows us to specify size, src has to be a URL.)\n<img src=\"https://github.com/stanford-crfm/helm/raw/main/src/helm/benchmark/static/images/helm-logo.png\" alt=\"\" width=\"800\"/>\n\nWelcome! The **`crfm-helm`** Python package contains code used in the **Holistic Evaluation of Language Models** project ([paper](https://arxiv.org/abs/2211.09110), [website](https://crfm.stanford.edu/helm/latest/)) by [Stanford CRFM](https://crfm.stanford.edu/). This package includes the following features:\n\n- Collection of datasets in a standard format (e.g., NaturalQuestions)\n- Collection of models accessible via a unified API (e.g., GPT-3, MT-NLG, OPT, BLOOM)\n- Collection of metrics beyond accuracy (efficiency, bias, toxicity, etc.)\n- Collection of perturbations for evaluating robustness and fairness (e.g., typos, dialect)\n- Modular framework for constructing prompts from datasets\n- Proxy server for managing accounts and providing unified interface to access models\n<!--intro-end-->\n\nTo get started, refer to [the documentation on Read the Docs](https://crfm-helm.readthedocs.io/) for how to install and run the package.\n\n## Directory Structure\n\nThe directory structure for this repo is as follows\n\n```\n├── docs # MD used to generate readthedocs\n│\n├── scripts # Python utility scripts for HELM\n│ ├── cache\n│ ├── data_overlap # Calculate train test overlap\n│ │ ├── common\n│ │ ├── scenarios\n│ │ └── test\n│ ├── efficiency\n│ ├── fact_completion\n│ ├── offline_eval\n│ └── scale\n└── src\n├── helm # Benchmarking Scripts for HELM\n│ │\n│ ├── benchmark # Main Python code for runn"},{"ref":"P27","kind":"page","title":"fw-ai/alpaca_eval repository metadata","date":"2026-06-11T03:02:05.816348+00:00","date_source":null,"source_url":"https://github.com/fw-ai/alpaca_eval","signal_url":null,"signal_json_url":null,"text":"# fw-ai/alpaca_eval\n\nDescription: An automatic evaluator for instruction-following language models. Human-validated, high-quality, cheap, and fast.\n\nLanguage: Jupyter Notebook\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2024-07-26T16:11:21Z\n\nPushed: 2024-07-26T17:34:43Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: tatsu-lab/alpaca_eval\n\nArchived: no\n\nREADME:\n# <a href=\"https://tatsu-lab.github.io/alpaca_eval/\" target=\"_blank\"><img src=\"https://raw.githubusercontent.com/tatsu-lab/alpaca_eval/main/docs/AlpacaFarm_small.png\" width=\"35\"></a> [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) : An Automatic Evaluator for Instruction-following Language Models\n\n[![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)](https://github.com/tatsu-lab/alpaca_farm/blob/main/LICENSE)\n[![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg)](https://github.com/tatsu-lab/alpaca_farm/blob/main/DATA_LICENSE)\n[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/release/python-3100/)\n[![discord](https://img.shields.io/badge/discord-server-blue?logo=discord&logoColor=white)](https://discord.gg/GJMxJSVZZM)\n\n**AlpacaEval 2.0 with length-controlled win-rates** ([paper](https://arxiv.org/abs/2404.04475)) has a spearman correlation of **0.98** with [ChatBot Arena](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard) while costing less than **$10** of OpenAI credits run and running in less than 3 minutes. Our goal is to have a benchmark for chat LLMs that is: fast (< 5min), cheap (< $10), and highly correlated with humans (0.98). Here's a comparison with other benchmarks:\n\n<p float=\"left\" align=\"middle\">\n<img src=\"figures/chat_correlations_no_ae.png\" alt=\"LC AlpacaEval is the most highly correlated benchmark with Chat Arena.\" width=\"500\"/>\n</p>\n\n---\n\nUpdates:\n\n:tada: **Length-controlled Win Rates** are out and used by default! This increases the correlation with ChatBot Arena from 0.93 to 0.98, while significantly decreasing length gameability. The raw win rates are still shown on the website and the CLI. More details [here](#length-contr"},{"ref":"P28","kind":"page","title":"fw-ai/cutlass repository metadata","date":"2026-06-11T03:02:00.554278+00:00","date_source":null,"source_url":"https://github.com/fw-ai/cutlass","signal_url":null,"signal_json_url":null,"text":"# fw-ai/cutlass\n\nDescription: CUDA Templates for Linear Algebra Subroutines\n\nLanguage: C++\n\nLicense: NOASSERTION\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2024-09-09T17:47:04Z\n\nPushed: 2025-04-04T20:38:53Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: NVIDIA/cutlass\n\nArchived: no\n\nREADME:\n![ALT](./media/images/gemm-hierarchy-with-epilogue-no-labels.png \"Complete CUDA GEMM decomposition\")\n\n# CUTLASS 3.9.0\n\n_CUTLASS 3.9.0 - March 2025_\n\nCUTLASS is a collection of CUDA C++ template abstractions for implementing\nhigh-performance matrix-matrix multiplication (GEMM) and related computations at all levels \nand scales within CUDA. It incorporates strategies for hierarchical decomposition and \ndata movement similar to those used to implement cuBLAS and cuDNN. CUTLASS decomposes \nthese \"moving parts\" into reusable, modular software components abstracted by C++ template \nclasses. Primitives for different levels of a conceptual parallelization hierarchy\ncan be specialized and tuned via custom tiling sizes, data types,\nand other algorithmic policy. The resulting flexibility simplifies their use\nas building blocks within custom kernels and applications.\n\nTo support a wide variety of applications, CUTLASS provides extensive support for\nmixed-precision computations, providing specialized data-movement and\nmultiply-accumulate abstractions for FP64, FP32, TF32, FP16, BF16,\n[FP32 emulation via tensor core instruction](./examples/27_ampere_3xtf32_fast_accurate_tensorop_gemm), \n8b floating point types (e5m2 and e4m3),\nblock scaled data types (NVIDIA NVFP4 and OCP standard MXFP4, MXFP6, MXFP8),\nnarrow integer types (4 and 8b signed and unsigned integers),\nand binary 1b data types (where architectures allow for the\nnative support of such data types).\nCUTLASS demonstrates optimal matrix multiply operations\ntargeting the programmable, high-throughput _Tensor Cores_ implemented by\nNVIDIA's Volta, Turing, Ampere, Ada, Hopper, and Blackwell architectures.\n\nIn addition to GEMMs, CUTLASS implements high-performance convolution via\nthe implicit GEMM algorithm. Implicit GEMM is the formulation of a convolution\noperation as a GEMM thereby taking advantage of CUTLASS's modular GEMM pi"},{"ref":"E1","kind":"event","title":"AI Field Engineer - Enterprise","date":"2026-06-13T05:20:27+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/fireworksai/jobs/4284317009","signal_url":"https://onlylabs.fyi/signals/8807e39b-09d4-473a-8c92-4d7dbf9e46ff","signal_json_url":"https://onlylabs.fyi/signals/8807e39b-09d4-473a-8c92-4d7dbf9e46ff/signal.json","text":"job_opened · AI Field Engineer - Enterprise · signal_desk=hiring · occurred_at=2026-06-13T05:20:27+00:00 · url=https://job-boards.greenhouse.io/fireworksai/jobs/4284317009 · raw={\"location\":\"New York, NY; Remote, USA; San Mateo, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E2","kind":"event","title":"AI Field Engineer - AI Natives","date":"2026-06-13T05:18:46+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/fireworksai/jobs/4280748009","signal_url":"https://onlylabs.fyi/signals/1694bffa-00ad-4abb-94e5-cb0bbc0ef7f3","signal_json_url":"https://onlylabs.fyi/signals/1694bffa-00ad-4abb-94e5-cb0bbc0ef7f3/signal.json","text":"job_opened · AI Field Engineer - AI Natives · signal_desk=hiring · occurred_at=2026-06-13T05:18:46+00:00 · url=https://job-boards.greenhouse.io/fireworksai/jobs/4280748009 · raw={\"location\":\"New York, NY; Remote, USA; San Mateo, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E3","kind":"event","title":"Field Marketing Manager, Startups","date":"2026-06-13T05:18:06+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/fireworksai/jobs/4256942009","signal_url":"https://onlylabs.fyi/signals/dd2ef48d-a792-49d6-9598-5efa2d56ad93","signal_json_url":"https://onlylabs.fyi/signals/dd2ef48d-a792-49d6-9598-5efa2d56ad93/signal.json","text":"job_opened · Field Marketing Manager, Startups · signal_desk=hiring · occurred_at=2026-06-13T05:18:06+00:00 · url=https://job-boards.greenhouse.io/fireworksai/jobs/4256942009 · raw={\"location\":\"San Mateo, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E4","kind":"event","title":"Sr Field Marketing Manager ","date":"2026-06-13T05:16:44+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/fireworksai/jobs/4257105009","signal_url":"https://onlylabs.fyi/signals/d9a87288-2919-4e7b-b6f8-5cda674991cc","signal_json_url":"https://onlylabs.fyi/signals/d9a87288-2919-4e7b-b6f8-5cda674991cc/signal.json","text":"job_opened · Sr Field Marketing Manager  · signal_desk=hiring · occurred_at=2026-06-13T05:16:44+00:00 · url=https://job-boards.greenhouse.io/fireworksai/jobs/4257105009 · raw={\"location\":\"New York, NY; San Mateo, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E5","kind":"event","title":"AI Field Engineer - Microsoft Foundry","date":"2026-06-13T04:47:38+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/fireworksai/jobs/4283962009","signal_url":"https://onlylabs.fyi/signals/433a5828-0c35-4e27-a2e5-eebf9de19815","signal_json_url":"https://onlylabs.fyi/signals/433a5828-0c35-4e27-a2e5-eebf9de19815/signal.json","text":"job_opened · AI Field Engineer - Microsoft Foundry · signal_desk=hiring · occurred_at=2026-06-13T04:47:38+00:00 · url=https://job-boards.greenhouse.io/fireworksai/jobs/4283962009 · raw={\"location\":\"San Mateo, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E6","kind":"event","title":"Security Engineer","date":"2026-06-12T21:40:12+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/fireworksai/jobs/4106639009","signal_url":"https://onlylabs.fyi/signals/6169a934-42ea-4521-beef-5bfec41adf63","signal_json_url":"https://onlylabs.fyi/signals/6169a934-42ea-4521-beef-5bfec41adf63/signal.json","text":"job_opened · Security Engineer · signal_desk=hiring · occurred_at=2026-06-12T21:40:12+00:00 · url=https://job-boards.greenhouse.io/fireworksai/jobs/4106639009 · raw={\"location\":\"San Mateo, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E7","kind":"event","title":"fw-ai/fireconnect","date":"2026-06-12T20:06:50+00:00","date_source":"source","source_url":"https://github.com/fw-ai/fireconnect","signal_url":"https://onlylabs.fyi/signals/9dcb297e-e3b8-4142-8c25-6f14dec774e9","signal_json_url":"https://onlylabs.fyi/signals/9dcb297e-e3b8-4142-8c25-6f14dec774e9/signal.json","text":"repo_new · fw-ai/fireconnect · signal_desk=repos · occurred_at=2026-06-12T20:06:50+00:00 · url=https://github.com/fw-ai/fireconnect · raw={\"repo\":\"fw-ai/fireconnect\",\"language\":\"JavaScript\"}"},{"ref":"E8","kind":"event","title":"Member of Technical Staff","date":"2026-06-11T20:55:59+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/fireworksai/jobs/4284300009","signal_url":"https://onlylabs.fyi/signals/478f44ac-d8f9-4c6a-b32e-e0dd917a9c00","signal_json_url":"https://onlylabs.fyi/signals/478f44ac-d8f9-4c6a-b32e-e0dd917a9c00/signal.json","text":"job_opened · Member of Technical Staff · signal_desk=hiring · occurred_at=2026-06-11T20:55:59+00:00 · url=https://job-boards.greenhouse.io/fireworksai/jobs/4284300009 · raw={\"location\":\"New York, NY\",\"ats\":\"greenhouse\"}"},{"ref":"E9","kind":"event","title":"Director, Revenue Strategy & 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