{"schema_version":"onlylabs.public_analysis_evidence.v1","title":"Baseten 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/baseten","json_url":"https://onlylabs.fyi/analysis/baseten/evidence.json","generated_at":"2026-06-11T18:06:13.749Z","org":{"slug":"baseten","name":"Baseten","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/baseten"},"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":140,"web":0,"evidence":88,"signal_desks":{"hiring":34,"forks":12,"releases":12,"talking":0,"repos":2},"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":"basetenlabs/run-report-action v1","date":"2026-06-11T04:05:53.476917+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/run-report-action/releases/tag/v1","signal_url":null,"signal_json_url":null,"text":"# v1\n\nRepository: basetenlabs/run-report-action\n\nTag: v1\n\nPublished: 2026-02-18T19:41:33Z\n\nPrerelease: no\n\nRelease notes: none published."},{"ref":"P2","kind":"page","title":"basetenlabs/action-junit-report repository metadata","date":"2026-06-11T02:54:18.745062+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/action-junit-report","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/action-junit-report\n\nDescription: Reports junit test results as GitHub Pull Request Check\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 0\n\nOpen issues: 6\n\nCreated: 2025-03-17T14:46:16Z\n\nPushed: 2026-02-07T12:48:30Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: mikepenz/action-junit-report\n\nArchived: no\n\nREADME:\n<div align=\"center\">\n:octocat:\n</div>\n<h1 align=\"center\">\naction-junit-report\n</h1>\n\n<p align=\"center\">\n... reports JUnit test results as GitHub pull request check.\n</p>\n\n<div align=\"center\">\n<img src=\".github/images/action.png\"/>\n</div>\n\n<div align=\"center\">\n<a href=\"https://github.com/mikepenz/action-junit-report\">\n<img src=\"https://github.com/mikepenz/action-junit-report/workflows/CI/badge.svg\"/>\n</a>\n</div>\n<br />\n\n-------\n\n<p align=\"center\">\n<a href=\"#whats-included-\">What's included 🚀</a> &bull;\n<a href=\"#setup\">Setup 🛠️</a> &bull;\n<a href=\"#sample-%EF%B8%8F\">Sample 🖥️</a> &bull;\n<a href=\"#contribute-\">Contribute 🧬</a> &bull;\n<a href=\"#license\">License 📓</a>\n</p>\n\n-------\n\n### What's included 🚀\n\n- Flexible JUnit parser with wide support\n- Supports nested test suites\n- Blazingly fast execution\n- Lighweight\n- Rich build log output\n\nThis action processes JUnit XML test reports on pull requests and shows the result as a PR check with summary and\nannotations.\n\nBased on action for [Surefire Reports by ScaCap](https://github.com/ScaCap/action-surefire-report)\n\n## Setup\n\n### Configure the workflow\n\n```yml\nname: build\non:\npull_request:\n\njobs:\nbuild:\nname: Build and Run Tests\nruns-on: ubuntu-latest\nsteps:\n- name: Checkout Code\nuses: actions/checkout@v4\n- name: Build and Run Tests\nrun: # execute your tests generating test results\n- name: Publish Test Report\nuses: mikepenz/action-junit-report@v5\nif: success() || failure() # always run even if the previous step fails\nwith:\nreport_paths: '**/build/test-results/test/TEST-*.xml'\n```\n\n### Inputs\n\n| **Input** | **Description** |\n|----------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `report_paths` | Optional. [Glob](https://"},{"ref":"P3","kind":"page","title":"basetenlabs/run-report-action repository metadata","date":"2026-06-11T02:43:10.121864+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/run-report-action","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/run-report-action\n\nDescription: A GitHub action for displaying a run report within a pull request.\n\nLanguage: TypeScript\n\nStars: 1\n\nForks: 0\n\nOpen issues: 7\n\nCreated: 2026-02-18T18:49:46Z\n\nPushed: 2026-05-22T10:43:40Z\n\nDefault branch: master\n\nFork: yes\n\nParent repository: moonrepo/run-report-action\n\nArchived: no\n\nREADME:\n# moon - CI run reports\n\nA GitHub action that reports the results of a `moon ci` run to a pull request as a comment and\nworkflow summary. The report will render all results, their final status, and time to completion, in\na [beautiful markdown table](#example).\n\nThe report will also include additional information about the environment, workflow matrix, and\ntouched files.\n\n## Installation\n\nThe action _must run after_ the `moon ci` command!\n\n```yaml\n# ...\njobs:\nci:\nname: CI\nruns-on: ubuntu-latest\nsteps:\n# ...\n- run: moon ci\n- uses: moonrepo/run-report-action@v1\nif: success() || failure()\nwith:\naccess-token: ${{ secrets.GITHUB_TOKEN }}\n```\n\nIf your workflow job is using a build matrix, you'll need to pass the entire matrix object as a JSON\nstring to the `matrix` input, otherwise the pull request comments will overwrite each other.\n\n```yaml\n# ...\njobs:\nci:\nname: CI\nruns-on: ${{ matrix.os }}\nstrategy:\nmatrix:\nos: [ubuntu-latest, windows-latest]\nnode-version: [16, 18]\nsteps:\n# ...\n- run: moon ci\n- uses: moonrepo/run-report-action@v1\nif: success() || failure()\nwith:\naccess-token: ${{ secrets.GITHUB_TOKEN }}\nmatrix: ${{ toJSON(matrix) }}\n```\n\n## Inputs\n\n- `access-token` (`string`) - REQUIRED: A GitHub access token that's used for posting comments on\nthe pull request.\n- `matrix` (`string`) - The workflow's build matrix as a JSON string. This is required for\ndifferentiating builds/comments.\n- `slow-threshold` (`number`) - Number of seconds before an action is to be considered slow.\nDefaults to 120 (2 minutes).\n- `sort-by` (`label | time`) - The field to sort the actions table on. If not defined (the default),\nwill display in the action graph's topological order.\n- `sort-dir` (`asc | desc`) - The direction to sort the actions table.\n- `workspace-root` (`string`) - Root of the moon workspace (if running in a sub-directory). Defaults\nto working "},{"ref":"P4","kind":"page","title":"basetenlabs/kaniko repository metadata","date":"2026-06-11T04:19:24.447966+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/kaniko","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/kaniko\n\nDescription: Build Container Images In Kubernetes\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2023-02-07T11:14:35Z\n\nPushed: 2023-05-12T19:44:30Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: GoogleContainerTools/kaniko\n\nArchived: no\n\nREADME:\n# kaniko - Build Images In Kubernetes\n\n## 🚨NOTE: kaniko is not an officially supported Google product🚨\n\n[![Unit tests](https://github.com/GoogleContainerTools/kaniko/actions/workflows/unit-tests.yaml/badge.svg)](https://github.com/GoogleContainerTools/kaniko/actions/workflows/unit-tests.yaml)\n[![Integration tests](https://github.com/GoogleContainerTools/kaniko/actions/workflows/integration-tests.yaml/badge.svg)](https://github.com/GoogleContainerTools/kaniko/actions/workflows/integration-tests.yaml)\n[![Build images](https://github.com/GoogleContainerTools/kaniko/actions/workflows/images.yaml/badge.svg)](https://github.com/GoogleContainerTools/kaniko/actions/workflows/images.yaml)\n[![Go Report Card](https://goreportcard.com/badge/github.com/GoogleContainerTools/kaniko)](https://goreportcard.com/report/github.com/GoogleContainerTools/kaniko)\n\n![kaniko logo](logo/Kaniko-Logo.png)\n\nkaniko is a tool to build container images from a Dockerfile, inside a container\nor Kubernetes cluster.\n\nkaniko doesn't depend on a Docker daemon and executes each command within a\nDockerfile completely in userspace. This enables building container images in\nenvironments that can't easily or securely run a Docker daemon, such as a\nstandard Kubernetes cluster.\n\nkaniko is meant to be run as an image: `gcr.io/kaniko-project/executor`. We do\n**not** recommend running the kaniko executor binary in another image, as it\nmight not work.\n\nWe'd love to hear from you! Join us on\n[#kaniko Kubernetes Slack](https://kubernetes.slack.com/messages/CQDCHGX7Y/)\n\n:mega: **Please fill out our\n[quick 5-question survey](https://forms.gle/HhZGEM33x4FUz9Qa6)** so that we can\nlearn how satisfied you are with kaniko, and what improvements we should make.\nThank you! :dancers:\n\n_If you are interested in contributing to kaniko, see\n[DEVELOPMENT.md](DEVELOPMENT.md) and [CONTRIBUTING.md](CONTRIBUTING.md)._\n\n<!-- START doctoc gen"},{"ref":"P5","kind":"page","title":"basetenlabs/pd-oncall-chat-topic repository metadata","date":"2026-06-11T04:19:24.423232+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/pd-oncall-chat-topic","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/pd-oncall-chat-topic\n\nDescription: AWS Lambda Function that updates a Chat Room topic (eg, Slack)\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2023-01-24T21:01:46Z\n\nPushed: 2022-11-08T22:18:50Z\n\nDefault branch: master\n\nFork: yes\n\nParent repository: PagerDuty/pd-oncall-chat-topic\n\nArchived: no\n\nREADME:\n# pd-oncall-chat-topic\nAWS Lambda Function that updates a Chat Room topic (eg, Slack)\n\n![Screenshot](https://raw.githubusercontent.com/PagerDuty/pd-oncall-chat-topic/master/screenshot.png)\n\n## Motivation\nAt [PagerDuty](https://www.pagerduty.com/), we\n[use](https://www.pagerduty.com/blog/how-does-pagerduty-use-pagerduty/)\nPagerDuty to manage our on-call schedules. One integration that we like is\nknowing who is on-call for a given team, via posting the information in a Slack\nchannel topic.\n\nAt PagerDuty Summit, one of our customers asked if we have this integration\nopen-sourced for them to use as well. At the time, we did not, it was deeply\nintegrated into our ChatOps tooling that is very specific to the PagerDuty\ninfrastructure. However, this integration is applicable to many other\norganizations.\n\nThis project started at one of our Hack Days, a day put aside monthly for\nemployees to build and present projects or ideas that fulfill some kind of need\nat the company.\n\n## How to Deploy\n1. Create an Integrated Bot that you can invite to your channel. https://my.slack.com/services/new/bot\n2. Obtain a PagerDuty API Key (v2) [Directions Here](https://support.pagerduty.com/docs/using-the-api#section-generating-an-api-key)\n3. Deploy CloudFormation\n- Clone repo\n- Modify 2 variables for your AWS Environment\n([Makefile#L1-L2](https://github.com/PagerDuty/pd-oncall-chat-topic/blob/master/Makefile#L1-L2))\n- `make deploy`\n4. Write API Keys to EC2 SSM Parameter Store.\n- The lambda function expects certain key names by default so the following\ncommands should work unless modified elsewhere (advanced config).\n- `make put-pd-key`\n- `make put-slack-key`\n5. Write Config to DynamoDB for which channels to update.\n- It is possible to use the AWS CLI for this (or finish\n[#4](https://github.com/PagerDuty/pd-oncall-chat-topic/issues/4) for ease of\nuse)\n- In "},{"ref":"P6","kind":"page","title":"basetenlabs/BasicSR repository metadata","date":"2026-06-11T04:19:24.28749+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/BasicSR","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/BasicSR\n\nDescription: Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2022-07-13T15:20:42Z\n\nPushed: 2022-07-13T15:39:58Z\n\nDefault branch: master\n\nFork: yes\n\nParent repository: XPixelGroup/BasicSR\n\nArchived: no\n\nREADME:\n<p align=\"center\">\n<img src=\"assets/basicsr_xpixel_logo.png\" height=120>\n</p>\n\n## <div align=\"center\"><b><a href=\"README.md\">English</a> | <a href=\"README_CN.md\">简体中文</a></b></div>\n\n[![LICENSE](https://img.shields.io/github/license/xinntao/basicsr.svg)](https://github.com/xinntao/BasicSR/blob/master/LICENSE.txt)\n[![PyPI](https://img.shields.io/pypi/v/basicsr)](https://pypi.org/project/basicsr/)\n[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/xinntao/BasicSR.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/xinntao/BasicSR/context:python)\n[![python lint](https://github.com/xinntao/BasicSR/actions/workflows/pylint.yml/badge.svg)](https://github.com/xinntao/BasicSR/blob/master/.github/workflows/pylint.yml)\n[![Publish-pip](https://github.com/xinntao/BasicSR/actions/workflows/publish-pip.yml/badge.svg)](https://github.com/xinntao/BasicSR/blob/master/.github/workflows/publish-pip.yml)\n[![gitee mirror](https://github.com/xinntao/BasicSR/actions/workflows/gitee-mirror.yml/badge.svg)](https://github.com/xinntao/BasicSR/blob/master/.github/workflows/gitee-mirror.yml)\n\n<div align=\"center\">\n\n⚡[**HowTo**](#-HOWTOs) **|** 🔧[**Installation**](docs/INSTALL.md) **|** 💻[**Training Commands**](docs/TrainTest.md) **|** 🐢[**DatasetPrepare**](docs/DatasetPreparation.md) **|** 🏰[**Model Zoo**](docs/ModelZoo.md)\n\n📕[**中文解读文档**](https://github.com/XPixelGroup/BasicSR-docs) **|** 📝[Introduction](docs/introduction.md) **|** <a href=\"https://github.com/XPixelGroup/BasicSR/tree/master/colab\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" height=\"18\" alt=\"google colab logo\"></a> **|** ⏳[TODO List](https://github.com/xinntao/BasicSR/projects) **|** ❓[FAQ"},{"ref":"P7","kind":"page","title":"basetenlabs/jinjasql repository metadata","date":"2026-06-11T04:19:24.238664+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/jinjasql","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/jinjasql\n\nDescription: Template Language for SQL with Automatic Bind Parameter Extraction\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2022-07-19T16:09:46Z\n\nPushed: 2022-07-19T19:57:10Z\n\nDefault branch: master\n\nFork: yes\n\nParent repository: sripathikrishnan/jinjasql\n\nArchived: no\n\nREADME:\n# Generate SQL Queries using a Jinja Template, without worrying about SQL Injection #\n\n[![Github Actions Build Status](https://github.com/sripathikrishnan/jinjasql/workflows/Tests/badge.svg)](https://github.com/sripathikrishnan/jinjasql/actions)\n\nJinjaSQL is a template language for SQL statements and scripts. \nSince it's based in [Jinja2](http://jinja.pocoo.org/), \nyou have all the power it offers - conditional statements, macros,\nlooping constructs, blocks, inheritance, and many more.\n\nJinjaSQL automatically binds parameters that are inserted into the template.\nAfter JinjaSQL evaluates the template, you get:\n\n1. A Query with %s placeholders for the parameters\n2. A List of values corresponding to the placeholders that need to be bound to the query\n\nJinjaSQL doesn't actually execute the query - it only prepares the \nquery and the bind parameters. You can execute the query using any \ndatabase engine / driver you are working with.\n\nFor example, if you have a template like this -\n\n```sql \nselect username, sum(spend)\nfrom transactions\nwhere start_date > {{request.start_date}}\nand end_date < {{request.end_date}}\n{% if request.organization %}\nand organization = {{request.organization}}\n{% endif %}\n```\n\nthen, depending on the parameters you provide, you get a query\n\n```sql\nselect username, sum(spend)\nfrom transaction\nwhere start_date > %s\nand end_date < %s\nand organization = %s\n```\nwith bind parameters = ['2016-10-10', '2016-10-20', 1321]\n\nIf `request.organization` was empty/falsy, the corresponding and clause\nwould be absent from the query, and the list of bind parameters\nwould not have the organization id.\n\n## When to use JinjaSQL ##\n\nJinjaSQL is *not* meant to replace your ORM. ORMs like those provided\nby SQLAlchemy or Django are great for a variety of use cases, and should\nbe the default in most cases. But there are a few use cases whe"},{"ref":"P8","kind":"page","title":"basetenlabs/riffusion-truss repository metadata","date":"2026-06-11T04:19:24.085136+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/riffusion-truss","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/riffusion-truss\n\nDescription: Stable diffusion for real-time music generation\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2022-12-16T19:51:12Z\n\nPushed: 2022-12-22T12:36:46Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: riffusion/riffusion-hobby\n\nArchived: no\n\nREADME:\n# Riffusion Truss\n\nRiffusion is an app for real-time music generation with stable diffusion.\n\nRead about it at https://www.riffusion.com/about and try it at https://www.riffusion.com/.\n\n* Web app: https://github.com/hmartiro/riffusion-app\n* Inference server: https://github.com/hmartiro/riffusion-inference\n* Model checkpoint: https://huggingface.co/riffusion/riffusion-model-v1\n\nThis repository contains the Python backend, packaged as a [Truss](https://truss.baseten.co), that runs the model inference and audio processing, including:\n\n* a diffusers pipeline that performs prompt interpolation combined with image conditioning\n* a module for (approximately) converting between spectrograms and waveforms\n* the configuration files for the Truss\n\n## Install\nTested with Python 3.9 and diffusers 0.9.0\n\n```\npip install -r dev_requirements.txt\npip install -r requirements.txt\nsudo apt update\nsudo apt install ffmpeg\n```\n\n## Run Riffusion Truss locally\n\nIf your local system has a GPU capable of running Riffusion, you can invoke it locally via Truss.\n\nAfter installing the necessary packages, open a Python shell or notebook in this directory and run:\n\n```python\nth = truss.from_directory(\"./riffusion/\")\ntest_req = {\n\"alpha\": 0.75,\n\"num_inference_steps\": 50,\n\"seed_image_id\": \"og_beat\",\n\n\"start\": {\n\"prompt\": \"church bells on sunday\",\n\"seed\": 42,\n\"denoising\": 0.75,\n\"guidance\": 7.0\n},\n\n\"end\": {\n\"prompt\": \"jazz with piano\",\n\"seed\": 123,\n\"denoising\": 0.75,\n\"guidance\": 7.0\n}\n}\nth.server_predict(test_req)\n```\n\nThis should give you an output formatted as follows:\n\n```json\n{\n\"image\": \"< base64 encoded JPEG image >\",\n\"audio\": \"< base64 encoded MP3 clip >\"\n}\n```\n\nFor the full APIs, see [InferenceInput](https://github.com/hmartiro/riffusion-inference/blob/main/riffusion/datatypes.py#L28) and [InferenceOutput](https://github.com/hmartiro/riffusion-inference/blob/main/riffusion/d"},{"ref":"P9","kind":"page","title":"basetenlabs/stablediffusion repository metadata","date":"2026-06-11T04:19:23.339788+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/stablediffusion","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/stablediffusion\n\nDescription: High-Resolution Image Synthesis with Latent Diffusion Models\n\nLicense: MIT\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2023-02-14T10:51:53Z\n\nPushed: 2023-02-21T17:03:00Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: nlile/stablediffusion\n\nArchived: no\n\nREADME:\n# Stable Diffusion Version 2\n![t2i](assets/stable-samples/txt2img/768/merged-0006.png)\n![t2i](assets/stable-samples/txt2img/768/merged-0002.png)\n![t2i](assets/stable-samples/txt2img/768/merged-0005.png)\n\nThis repository contains [Stable Diffusion](https://github.com/CompVis/stable-diffusion) models trained from scratch and will be continuously updated with\nnew checkpoints. The following list provides an overview of all currently available models. More coming soon.\n\n## News\n\n**December 7, 2022**\n\n*Version 2.1*\n\n- New stable diffusion model (_Stable Diffusion 2.1-v_, [HuggingFace](https://huggingface.co/stabilityai/stable-diffusion-2-1)) at 768x768 resolution and (_Stable Diffusion 2.1-base_, [HuggingFace](https://huggingface.co/stabilityai/stable-diffusion-2-1-base)) at 512x512 resolution, both based on the same number of parameters and architecture as 2.0 and fine-tuned on 2.0, on a less restrictive NSFW filtering of the [LAION-5B](https://laion.ai/blog/laion-5b/) dataset.\nPer default, the attention operation of the model is evaluated at full precision when `xformers` is not installed. To enable fp16 (which can cause numerical instabilities with the vanilla attention module on the v2.1 model) , run your script with `ATTN_PRECISION=fp16 python <thescript.py>`\n\n**November 24, 2022**\n\n*Version 2.0*\n\n- New stable diffusion model (_Stable Diffusion 2.0-v_) at 768x768 resolution. Same number of parameters in the U-Net as 1.5, but uses [OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip) as the text encoder and is trained from scratch. _SD 2.0-v_ is a so-called [v-prediction](https://arxiv.org/abs/2202.00512) model. \n- The above model is finetuned from _SD 2.0-base_, which was trained as a standard noise-prediction model on 512x512 images and is also made available.\n- Added a [x4 upscaling latent text-guided diffusion model](#image-upscaling-with-stable"},{"ref":"P10","kind":"page","title":"basetenlabs/stable-diffusion repository metadata","date":"2026-06-11T04:19:23.281258+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/stable-diffusion","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/stable-diffusion\n\nDescription: A latent text-to-image diffusion model\n\nLicense: NOASSERTION\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2023-02-10T17:41:31Z\n\nPushed: 2023-02-13T22:40:54Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: CompVis/stable-diffusion\n\nArchived: no\n\nREADME:\n# Stable Diffusion\n*Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:*\n\n[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)<br/>\n[Robin Rombach](https://github.com/rromb)\\*,\n[Andreas Blattmann](https://github.com/ablattmann)\\*,\n[Dominik Lorenz](https://github.com/qp-qp)\\,\n[Patrick Esser](https://github.com/pesser),\n[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/>\n_[CVPR '22 Oral](https://openaccess.thecvf.com/content/CVPR2022/html/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.html) |\n[GitHub](https://github.com/CompVis/latent-diffusion) | [arXiv](https://arxiv.org/abs/2112.10752) | [Project page](https://ommer-lab.com/research/latent-diffusion-models/)_\n\n![txt2img-stable2](assets/stable-samples/txt2img/merged-0006.png)\n[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion\nmodel.\nThanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. \nSimilar to Google's [Imagen](https://arxiv.org/abs/2205.11487), \nthis model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.\nWith its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.\nSee [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).\n\n## Requirements\nA suitable [conda](https://conda.io/) environment named `ldm` can be created\nand activated with:\n\n```\nconda env create -f environment.yaml\nconda acti"},{"ref":"P11","kind":"page","title":"basetenlabs/langchain repository metadata","date":"2026-06-11T04:19:23.222478+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/langchain","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/langchain\n\nDescription: ⚡ Building applications with LLMs through composability ⚡\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 0\n\nForks: 2\n\nOpen issues: 0\n\nCreated: 2023-05-03T13:40:45Z\n\nPushed: 2023-12-22T21:04:32Z\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/check_diffs.yml/badge.svg)](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.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)\n\nLooking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).\n\nTo help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com"},{"ref":"P12","kind":"page","title":"basetenlabs/ControlNet repository metadata","date":"2026-06-11T04:19:23.214243+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/ControlNet","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/ControlNet\n\nDescription: Let us control diffusion models\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 1\n\nForks: 0\n\nOpen issues: 1\n\nCreated: 2023-02-20T22:52:20Z\n\nPushed: 2023-04-04T15:45:54Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: lllyasviel/ControlNet\n\nArchived: no\n\nREADME:\n# ControlNet\n\nOfficial implementation of [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543).\n\nControlNet is a neural network structure to control diffusion models by adding extra conditions.\n\n![img](github_page/he.png)\n\nIt copys the weights of neural network blocks into a \"locked\" copy and a \"trainable\" copy. \n\nThe \"trainable\" one learns your condition. The \"locked\" one preserves your model. \n\nThanks to this, training with small dataset of image pairs will not destroy the production-ready diffusion models.\n\nThe \"zero convolution\" is 1×1 convolution with both weight and bias initialized as zeros. \n\nBefore training, all zero convolutions output zeros, and ControlNet will not cause any distortion.\n\nNo layer is trained from scratch. You are still fine-tuning. Your original model is safe. \n\nThis allows training on small-scale or even personal devices.\n\nThis is also friendly to merge/replacement/offsetting of models/weights/blocks/layers.\n\n### FAQ\n\n**Q:** But wait, if the weight of a conv layer is zero, the gradient will also be zero, and the network will not learn anything. Why \"zero convolution\" works?\n\n**A:** This is not true. [See an explanation here](docs/faq.md).\n\n# Stable Diffusion + ControlNet\n\nBy repeating the above simple structure 14 times, we can control stable diffusion in this way:\n\n![img](github_page/sd.png)\n\nNote that the way we connect layers is computational efficient. The original SD encoder does not need to store gradients (the locked original SD Encoder Block 1234 and Middle). The required GPU memory is not much larger than original SD, although many layers are added. Great!\n\n# Production-Ready Pretrained Models\n\nFirst create a new conda environment\n\nconda env create -f environment.yaml\nconda activate control\n\nAll models and detectors can be downloaded from [our Hugging Face page](https://huggingface.co"},{"ref":"P13","kind":"page","title":"basetenlabs/starcoder-truss repository metadata","date":"2026-06-11T04:19:23.21417+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/starcoder-truss","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/starcoder-truss\n\nDescription: Truss for deploying Starcoder to Baseten or other platforms\n\nLanguage: Python\n\nStars: 12\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2023-05-07T22:16:32Z\n\nPushed: 2023-06-15T20:07:49Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: squidarth/starcoder-truss\n\nArchived: yes\n\nREADME:\n# Starcoder Truss\n\nThis is a [Truss](https://truss.baseten.co/) for Starcoder. Starcoder is an open-source language model trained specifically\nfor code auto-completions. It was trained on text from over 80 programming languages. Check out more info about this model\n[here](https://huggingface.co/bigcode/starcoder).\n\nBefore deploying this model, you'll need to:\n\n1. Accept the terms of service of the Starcoder model [here](https://huggingface.co/bigcode/starcoder).\n2. Retrieve your Huggingface token from the [settings](https://huggingface.co/settings/tokens).\n3. Set your Huggingface token as a Baseten secret [here](https://app.baseten.co/settings/secrets) with the key `hf_api_key`. Note that you will *not* be able to successfully deploy Starcoder without doing this.\n\n## Deploying Starcoder \n\nTo deploy the Starcoder Truss, you'll need to follow these steps:\n\n1. __Prerequisites__: Make sure you have a Baseten account and API key. You can sign up for a Baseten account [here](https://app.baseten.co/signup).\n\n2. __Install Truss and the Baseten Python client__: If you haven't already, install the Baseten Python client and Truss in your development environment using:\n```\npip install --upgrade baseten truss\n```\n\n3. __Load the Starcoder Truss__: Assuming you've cloned this repo, spin up an IPython shell and load the Truss into memory:\n\nNote this assumes that you started the ipython shell from root of the repo.\n\n```\nimport truss\n\nstarcoder_truss = truss.load(\".\")\n```\n\n4. __Log in to Baseten__: Log in to your Baseten account using your API key (key found [here](https://app.baseten.co/settings/account/api_keys)):\n```\nimport baseten\n\nbaseten.login(\"PASTE_API_KEY_HERE\")\n```\n\n5. __Deploy the Starcoder Truss__: Deploy the Starcoder Truss to Baseten with the following command:\n```\nbaseten.deploy(starcoder_truss)\n```\n\nOnce your Truss is deployed, you can start using"},{"ref":"P14","kind":"page","title":"basetenlabs/mpt-7b-base-truss repository metadata","date":"2026-06-11T04:19:21.089087+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/mpt-7b-base-truss","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/mpt-7b-base-truss\n\nDescription: A deployment \"truss\" for the MPT-7B Base model from MosaicML\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 1\n\nOpen issues: 2\n\nCreated: 2023-05-23T21:01:44Z\n\nPushed: 2023-07-23T16:16:34Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: tuhins/mpt-7b-instruct-truss\n\nArchived: yes\n\nREADME:\n# MPT-7B Base Truss\n\nThis is a [Truss](https://truss.baseten.co/) for [MPT-7B](https://www.mosaicml.com/blog/mpt-7b) using the `mosaicml/mpt-7b` family of models from the `transformers` library. This README will walk you through how to deploy the base version on Baseten.\n\n## Truss\n\nTruss is an open-source model serving framework developed by Baseten. It allows you to develop and deploy machine learning models onto Baseten (and other platforms like [AWS](https://truss.baseten.co/deploy/aws) or [GCP](https://truss.baseten.co/deploy/gcp). Using Truss, you can develop a GPU model using [live-reload](https://baseten.co/blog/technical-deep-dive-truss-live-reload), package models and their associated code, create Docker containers and deploy on Baseten.\n\n## Deploying MPT-7B\n\nTo deploy the MPT-7B Base Truss, you'll need to follow these steps:\n\n1. __Prerequisites__: Make sure you have a Baseten account and API key. You can sign up for a Baseten account [here](https://app.baseten.co/signup).\n\n2. __Install Truss and the Baseten Python client__: If you haven't already, install the Baseten Python client and Truss in your development environment using:\n```\npip install --upgrade baseten truss\n```\n\n3. __Load the MPT-7B Base Truss__: Assuming you've cloned this repo, spin up an IPython shell and load the Truss into memory:\n```\nimport truss\nmpt_truss = truss.load(\"path/to/mpt_truss\")\n```\n\n4. __Log in to Baseten__: Log in to your Baseten account using your API key (key found [here](https://app.baseten.co/settings/account/api_keys)):\n```\nimport baseten\n\nbaseten.login(\"PASTE_API_KEY_HERE\")\n```\n\n5. __Deploy the MPT-7B Base Truss__: Deploy MPT-7B Base to Baseten with the following command:\n```\nbaseten.deploy(mpt_truss)\n```\n\nOnce your Truss is deployed, you can start using MPT-7B Base through the Baseten platform! Navigate to the Baseten UI to"},{"ref":"P15","kind":"page","title":"basetenlabs/wizardlm-truss repository metadata","date":"2026-06-11T04:19:20.379417+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/wizardlm-truss","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/wizardlm-truss\n\nLanguage: Python\n\nStars: 11\n\nForks: 2\n\nOpen issues: 1\n\nCreated: 2023-05-26T05:55:30Z\n\nPushed: 2023-07-23T16:15:00Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: basetenlabs/stablelm-truss\n\nArchived: yes\n\nREADME:\n# WizardLM Truss\n\nThis repository packages [WizardLM](https://github.com/nlpxucan/WizardLM) as a [Truss](https://truss.baseten.co).\n\nWizardLM is a instruction-following LLM tuned using the Evol-Instruct method. Evol-Instruct is a novel method using LLMs instead of humans to automatically mass-produce open-domain instructions of various difficulty levels and skills range, to improve the performance of LLMs.\n\nUtilizing this model for inference can be challenging given the hardware requirements. With Baseten and Truss, inference is dead simple.\n\n## Deploying WizardLM\n\nWe found this model runs reasonably fast on A10Gs; you can configure the hardware you'd like in the `config.yaml`.\n\n```yaml\n...\nresources:\ncpu: \"3\"\nmemory: 14Gi\nuse_gpu: true\naccelerator: A10G\n...\n```\n\nBefore deployment:\n\n1. Make sure you have a Baseten account and API key. You can sign up for a Baseten account [here](https://app.baseten.co/signup).\n2. Install Truss and the Baseten Python client: `pip install --upgrade baseten truss`\n3. Authenticate your development environment with `baseten login`\n\nDeploying the Truss is easy; simply load it and push from a Python script:\n\n```python\nimport baseten\nimport truss\n\nwizardlm_truss = truss.load('.')\nbaseten.deploy(wizardlm_truss)\n```\n\n## Invoking WizardLM\n\nThe usual GPT-style parameters will pass right through to the inference point:\n\n* max_new_tokens (_default_: 64)\n* temperature (_default_: 0.5)\n* top_p (_default_: 0.9)\n* top_k (_default_: 0)\n* num_beams (_default_: 4)\n\n```python\nimport baseten\nmodel = baseten.deployed_model_id('YOUR MODEL ID')\nmodel.predict({\"prompt\": \"What is the difference between a wizard and a sorcerer?\"})\n```\n\nYou can also invoke your model via a REST API\n\n```\ncurl -X POST \" https://app.baseten.co/models/YOUR_MODEL_ID/predict\" \\\n-H \"Content-Type: application/json\" \\\n-H 'Authorization: Api-Key {YOUR_API_KEY}' \\\n-d '{\n\"prompt\": \"What is the difference between a wizard and a sorcerer?\",\n\"te"},{"ref":"P16","kind":"page","title":"basetenlabs/wizardlm-truss-1 repository metadata","date":"2026-06-11T04:19:20.02429+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/wizardlm-truss-1","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/wizardlm-truss-1\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2023-06-30T00:04:05Z\n\nPushed: 2023-06-29T14:02:35Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: basetenlabs/wizardlm-truss\n\nArchived: yes\n\nREADME:\n# WizardLM Truss\n\nThis repository packages [WizardLM](https://github.com/nlpxucan/WizardLM) as a [Truss](https://truss.baseten.co).\n\nWizardLM is a instruction-following LLM tuned using the Evol-Instruct method. Evol-Instruct is a novel method using LLMs instead of humans to automatically mass-produce open-domain instructions of various difficulty levels and skills range, to improve the performance of LLMs.\n\nUtilizing this model for inference can be challenging given the hardware requirements. With Baseten and Truss, inference is dead simple.\n\n## Deploying WizardLM\n\nWe found this model runs reasonably fast on A10Gs; you can configure the hardware you'd like in the `config.yaml`.\n\n```yaml\n...\nresources:\ncpu: \"3\"\nmemory: 14Gi\nuse_gpu: true\naccelerator: A10G\n...\n```\n\nBefore deployment:\n\n1. Make sure you have a Baseten account and API key. You can sign up for a Baseten account [here](https://app.baseten.co/signup).\n2. Install Truss and the Baseten Python client: `pip install --upgrade baseten truss`\n3. Authenticate your development environment with `baseten login`\n\nDeploying the Truss is easy; simply load it and push from a Python script:\n\n```python\nimport baseten\nimport truss\n\nwizardlm_truss = truss.load('.')\nbaseten.deploy(wizardlm_truss)\n```\n\n## Invoking WizardLM\n\nThe usual GPT-style parameters will pass right through to the inference point:\n\n* max_new_tokens (_default_: 64)\n* temperature (_default_: 0.5)\n* top_p (_default_: 0.9)\n* top_k (_default_: 0)\n* num_beams (_default_: 4)\n\n```python\nimport baseten\nmodel = baseten.deployed_model_id('YOUR MODEL ID')\nmodel.predict({\"prompt\": \"What is the difference between a wizard and a sorcerer?\"})\n```\n\nYou can also invoke your model via a REST API\n\n```\ncurl -X POST \" https://app.baseten.co/models/YOUR_MODEL_ID/predict\" \\\n-H \"Content-Type: application/json\" \\\n-H 'Authorization: Api-Key {YOUR_API_KEY}' \\\n-d '{\n\"prompt\": \"What is the difference between a wizard and a sorcerer?\",\n\"temperature\": 0.3\n}"},{"ref":"P17","kind":"page","title":"basetenlabs/diffusers repository metadata","date":"2026-06-11T04:19:19.978807+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/diffusers","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/diffusers\n\nDescription: 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2023-08-23T20:02:06Z\n\nPushed: 2023-11-27T19:31:26Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: huggingface/diffusers\n\nArchived: no\n\nREADME:\n<!---\nCopyright 2022 - The HuggingFace Team. All rights reserved.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\nhttp://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n-->\n\n<p align=\"center\">\n<br>\n<img src=\"https://raw.githubusercontent.com/huggingface/diffusers/main/docs/source/en/imgs/diffusers_library.jpg\" width=\"400\"/>\n<br>\n<p>\n<p align=\"center\">\n<a href=\"https://github.com/huggingface/diffusers/blob/main/LICENSE\">\n<img alt=\"GitHub\" src=\"https://img.shields.io/github/license/huggingface/datasets.svg?color=blue\">\n</a>\n<a href=\"https://github.com/huggingface/diffusers/releases\">\n<img alt=\"GitHub release\" src=\"https://img.shields.io/github/release/huggingface/diffusers.svg\">\n</a>\n<a href=\"https://pepy.tech/project/diffusers\">\n<img alt=\"GitHub release\" src=\"https://static.pepy.tech/badge/diffusers/month\">\n</a>\n<a href=\"CODE_OF_CONDUCT.md\">\n<img alt=\"Contributor Covenant\" src=\"https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg\">\n</a>\n<a href=\"https://twitter.com/diffuserslib\">\n<img alt=\"X account\" src=\"https://img.shields.io/twitter/url/https/twitter.com/diffuserslib.svg?style=social&label=Follow%20%40diffuserslib\">\n</a>\n</p>\n\n🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, 🤗 Diffusers is"},{"ref":"P18","kind":"page","title":"basetenlabs/chainlit-cookbook repository metadata","date":"2026-06-11T04:19:19.977482+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/chainlit-cookbook","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/chainlit-cookbook\n\nDescription: Chainlit's cookbook repo\n\nLanguage: Python\n\nStars: 0\n\nForks: 1\n\nOpen issues: 0\n\nCreated: 2023-08-03T22:53:15Z\n\nPushed: 2023-08-17T20:47:59Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: Chainlit/cookbook\n\nArchived: no\n\nREADME:\n# Chainlit Cookbook\n\nWelcome to the Chainlit Demos repository! Here you'll find a collection of example projects demonstrating how to use Chainlit to create amazing chatbot UIs with ease. Each folder in this repository represents a separate demo project.\n\n## 🚀 Getting Started\n\nTo run a demo, follow these steps:\n\n1. Clone this repository:\n```\ngit clone https://github.com/Chainlit/cookbook.git chainlit-cookbook\n```\n2. Navigate to the desired demo folder:\n```\ncd chainlit-cookbook/demo-folder-name\n```\n3. Install the required dependencies:\n```\npip install -r requirements.txt\n```\n4. Create a `.env` file based on the provided `.env.example` file:\n```\ncp .env.example .env\n```\nModify the `.env` file as needed to include any necessary API keys or configuration settings.\n5. Run the Chainlit app in watch mode:\n```\nchainlit run app.py -w\n```\n\nYour demo chatbot UI should now be up and running in your browser!\n\n## 💁 Contributing\n\nWe'd love to see more demos showcasing the power of Chainlit. If you have an idea for a demo or want to contribute one, please feel free to open an issue or create a pull request. Your contributions are highly appreciated!"},{"ref":"P19","kind":"page","title":"basetenlabs/triton-inference-server repository metadata","date":"2026-06-11T04:19:19.10242+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/triton-inference-server","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/triton-inference-server\n\nDescription: The Triton Inference Server provides an optimized cloud and edge inferencing solution. \n\nLanguage: Python\n\nLicense: BSD-3-Clause\n\nStars: 1\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2024-01-09T17:50:05Z\n\nPushed: 2024-01-11T22:20:17Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: triton-inference-server/server\n\nArchived: no\n\nREADME:\n<!--\n# Copyright 2018-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n# * Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# * Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the distribution.\n# * Neither the name of NVIDIA CORPORATION nor the names of its\n# contributors may be used to endorse or promote products derived\n# from this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY\n# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\n# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR\n# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\n# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\n# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY\n# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n-->\n\n# Triton Inference Server\n\n[![License](https://img.shields.io/badge/License-BSD3-lightgrey.svg)](https://opensource.org/licenses/BSD-3-Clause)\n\n> [!WARNING]\n> ##### LATEST RELEASE\n> You are currently on the `main` branch which tracks under-development progress towards the next"},{"ref":"P20","kind":"page","title":"basetenlabs/python_backend repository metadata","date":"2026-06-11T04:19:18.696131+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/python_backend","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/python_backend\n\nDescription: Triton backend that enables pre-process, post-processing and other logic to be implemented in Python.\n\nLicense: BSD-3-Clause\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2024-01-09T17:51:19Z\n\nPushed: 2024-01-11T13:59:19Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: triton-inference-server/python_backend\n\nArchived: no\n\nREADME:\n<!--\n# Copyright 2020-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n# * Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# * Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the distribution.\n# * Neither the name of NVIDIA CORPORATION nor the names of its\n# contributors may be used to endorse or promote products derived\n# from this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY\n# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\n# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR\n# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\n# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\n# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY\n# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n-->\n\n[![License](https://img.shields.io/badge/License-BSD3-lightgrey.svg)](https://opensource.org/licenses/BSD-3-Clause)\n\n# Python Backend\n\nThe Triton backend for Python. The goal of Python backend is to let you serve\nmodels written in Python by Triton Inference Server without having to"},{"ref":"P21","kind":"page","title":"basetenlabs/tensorrtllm_backend repository metadata","date":"2026-06-11T04:19:18.342564+00:00","date_source":null,"source_url":"https://github.com/basetenlabs/tensorrtllm_backend","signal_url":null,"signal_json_url":null,"text":"# basetenlabs/tensorrtllm_backend\n\nDescription: The Triton TensorRT-LLM Backend\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2024-01-09T17:52:34Z\n\nPushed: 2024-01-11T21:06:34Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: triton-inference-server/tensorrtllm_backend\n\nArchived: no\n\nREADME:\n<!--\n# Copyright 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n# * Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# * Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the distribution.\n# * Neither the name of NVIDIA CORPORATION nor the names of its\n# contributors may be used to endorse or promote products derived\n# from this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY\n# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\n# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR\n# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\n# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\n# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY\n# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n-->\n\n# TensorRT-LLM Backend\nThe Triton backend for [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM).\nYou can learn more about Triton backends in the [backend repo](https://github.com/triton-inference-server/backend).\nThe goal of TensorRT-LLM Backend is to let you serve [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM)\nmodels with Triton Infe"},{"ref":"P22","kind":"page","title":"Engineering Manager, Internal Platform","date":"2026-06-11T04:13:21.894+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/baseten/5feb08e9-d81b-43bd-a694-f4b1a71992a6","signal_url":null,"signal_json_url":null,"text":"Jobs"},{"ref":"P23","kind":"page","title":"Engineering Manager, Cloud Platform","date":"2026-06-11T04:13:21.855+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/baseten/1933097e-2f8e-46e2-8442-0c2f4a9394da","signal_url":null,"signal_json_url":null,"text":"Jobs"},{"ref":"P24","kind":"page","title":"Recruiting Operations Lead","date":"2026-06-11T04:13:19.439+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/baseten/8d362149-b684-43b2-a180-d2ae4ae2dfc2","signal_url":null,"signal_json_url":null,"text":"Recruiting Operations Lead @ Baseten"},{"ref":"P25","kind":"page","title":"Forward Deployed Engineer","date":"2026-06-11T04:12:59.998266+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/baseten/84c1801c-1a65-49fb-aaaa-beeafd530e7e","signal_url":null,"signal_json_url":null,"text":"# Forward Deployed Engineer\n\nTeam: FDE Generalists\n\nLocation: San Francisco\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2024-03-28T21:00:55.921+00:00\n\nABOUT BASETEN\n\nBaseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $300M Series E https://www.baseten.co/blog/announcing-baseten-s-300m-series-e/, backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction. Join us and help build the platform engineers turn to to ship AI products.\n\nTHE ROLE\n\nAs a Forward Deployed Engineer at Baseten, you will partner directly with customers to architect, build, and deploy high-scale production AI applications on Baseten’s platform. You’ll own the journey with customers from initial exploration to production deployment, translating ambiguous business goals into reliable, observable services with clear quality, latency, and cost outcomes.\n\nThis role is a great fit for entrepreneurial engineers who want a front-row view into how modern companies adopt AI at scale and who enjoy working across product, software development, performance engineering, and customer-facing implementations.\n\nTo be clear, this is an engineering role with hands-on coding and software development that also includes aspects of product management, technical customer success, and pre-sales solution engineering mixed in.\n\nEXAMPLE INITIATIVES\n\nTake a look at these blog posts written by members of our Forward Deployed Engineering team:\n\n- Forward Deployed Engineering on the frontier of AI https://www.baseten.co/blog/forward-deployed-engineering/\n\n- The fastest, most accurate Whisper transcription https://www.baseten.co/blog/the-fastest-most-accurate-and-cost-efficient-whisper-transcription/\n\n- Deploy production-ready model servers from Docker images https://www.baseten.co/blog/deploy-production-model-servers-from-docker-images/\n\n- Deplo"},{"ref":"P26","kind":"page","title":"Software Engineer - Model Performance","date":"2026-06-11T04:12:59.996621+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/baseten/d29e748c-7209-460d-a024-8f77ae0a3d4d","signal_url":null,"signal_json_url":null,"text":"# Software Engineer - Model Performance\n\nTeam: Model Performance\n\nLocation: San Francisco\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2024-03-28T21:01:24.780+00:00\n\nABOUT BASETEN\n\nBaseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $300M Series E https://www.baseten.co/blog/announcing-baseten-s-300m-series-e/, backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction. Join us and help build the platform engineers turn to to ship AI products.\n\nTHE ROLE\n\nAre you passionate about advancing the application of artificial intelligence? We are looking for a Software Engineer focused on ML performance to join our dynamic team. This role is ideal for someone who thrives in a fast-paced startup environment and is eager to make significant contributions to the exciting field of LLM Inference. If you are a backend engineer who thrives on making things faster and is excited about open-source ML models, we look forward to your application.\n\nEXAMPLE INITIATIVES\n\nYou'll get to work on these types of projects as part of our Model Performance team:\n\n- Baseten Embeddings Inference: The fastest embeddings solution available https://www.baseten.co/blog/introducing-baseten-embeddings-inference-bei/\n\n- The Baseten Inference Stack https://www.baseten.co/resources/guide/the-baseten-inference-stack/\n\n- Driving model performance optimization https://www.baseten.co/blog/driving-model-performance-optimization-2024-highlights/\n\nRESPONSIBILITIES\n\n- Implement, refine, and productionize cutting-edge techniques (quantization, speculative decoding, kv cache reuse, chunked prefill and LoRA) for ML model inference and infrastructure.\n\n- Deep dive into underlying codebases of TensorRT, PyTorch, TensorRT-LLM, vllm, sglang, CUDA, and other libraries to debug ML performance issues.\n\n- Apply and scale optimization techniq"},{"ref":"P27","kind":"page","title":"Product Engineer - Dedicated Inference ","date":"2026-06-11T04:12:59.854806+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/baseten/fc6e5f2e-eb2d-4a6c-8a51-8422e8662bde","signal_url":null,"signal_json_url":null,"text":"# Product Engineer - Dedicated Inference \n\nTeam: Dedicated Inference\n\nLocation: San Francisco\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2024-07-09T17:24:19.832+00:00\n\nABOUT BASETEN\n\nBaseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $300M Series E https://www.baseten.co/blog/announcing-baseten-s-300m-series-e/, backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction. Join us and help build the platform engineers turn to to ship AI products.\n\nTHE ROLE\n\nAs a Product Engineer on the Dedicated Inference team, you'll shape the state-of-the-art developer experience for deploying and operating AI workloads in production. From the CLI and SDKs to APIs, observability, and debugging workflows, you'll build the tools customers rely on every day to manage mission-critical inference deployments.\n\nFew teams at Baseten have as much breadth and visibility as Dedicated Inference. The team is often at the forefront of new product development, giving engineers the opportunity to shape the experience of some of our most important customers.\n\nEXAMPLE INITIATIVES\n\nYou'll get to work on these types of projects as part of our Dedicated Inference team:\n\n- Chains for multi-component workflows https://www.baseten.co/blog/baseten-chains-explained/\n\n- Asynchronous inference https://www.baseten.co/blog/using-asynchronous-inference-in-production/\n\n- Model APIs for frontier models https://www.baseten.co/products/model-apis/\n\n- Model training built for production inference https://www.baseten.co/products/training/\n\nRESPONSIBILITIES\n\n- Implement new features and products for the team\n\n- Design ergonomic APIs and abstractions to solve customer problems\n\n- Fix bugs and resolve customer issues with urgency\n\n- Work across the stack - regardless of where you start, you’ll end up touching both React Components and "},{"ref":"P28","kind":"page","title":"Sales Development Representative","date":"2026-06-11T04:12:59.848328+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/baseten/fdbbf773-0857-4771-8b90-f3dfa1d8683f","signal_url":null,"signal_json_url":null,"text":"# Sales Development Representative\n\nTeam: Sales and Business Development\n\nLocation: New York\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2025-02-05T22:29:16.013+00:00\n\nABOUT BASETEN\n\nBaseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $300M Series E https://www.baseten.co/blog/announcing-baseten-s-300m-series-e/, backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction. Join us and help build the platform engineers turn to to ship AI products.\n\nTHE ROLE\n\nAs a Sales Development Representative at Baseten, you will be helping build a qualified pipeline by engaging with potential customers. This includes outbound and inbound prospecting via phone, e-mail, LinkedIn, or whatever it takes to get them excited about Baseten. In this role, you will work alongside a sales and marketing team that focuses on the business needs of our customers.\n\nRESPONSIBILITIES\n\n- Build revenue pipeline by setting introductory meetings with potential businesses and key decision makers.\n\n- Diligently respond to inbound inquiries and determine potential product fit.\n\n- Help influence Baseten's product roadmap for customers and prospects.\n\n- Identify high-potential businesses and verticals and develop and execute outbound strategies to bring them to Baseten.\n\n- Stay up-to-date on market trends, competition, and industry developments.\n\n- Manage and document the progression of the sales pipeline.\n\n- Drive pre- & post-engagement at industry events (will attend multiple events in person).\n\nREQUIREMENTS\n\n- 9+ months of experience in a sales/lead generation role in a high-tech/SaaS company.\n\n- Ability to develop strong, long-lasting relationships both internally and externally.\n\n- Collaborative and coachable, always looking to improve your skills and impact.\n\n- Ability to handle rejection and stay persistent in pursu"},{"ref":"E1","kind":"event","title":"Product Manager, Developer Experience","date":"2026-06-10T20:43:54.991+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/baseten/2d78fdcf-53e1-45d3-a047-2aefb5ad3153","signal_url":"https://onlylabs.fyi/signals/9fdd79ab-e89f-4aee-94e6-cd1cabd7542b","signal_json_url":"https://onlylabs.fyi/signals/9fdd79ab-e89f-4aee-94e6-cd1cabd7542b/signal.json","text":"job_opened · Product Manager, Developer Experience · signal_desk=hiring · occurred_at=2026-06-10T20:43:54.991+00:00 · url=https://jobs.ashbyhq.com/baseten/2d78fdcf-53e1-45d3-a047-2aefb5ad3153 · raw={\"location\":\"San Francisco\",\"team\":\"Product\",\"ats\":\"ashby\"}"},{"ref":"E2","kind":"event","title":"basetenlabs/truss 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Infrastructure · signal_desk=hiring · occurred_at=2026-06-10T01:04:24.882+00:00 · url=https://jobs.ashbyhq.com/baseten/7d9d5a1f-3834-434e-b22f-4bd62317be3c · raw={\"location\":\"San Francisco\",\"team\":\"Infrastructure\",\"ats\":\"ashby\"}"},{"ref":"E4","kind":"event","title":"basetenlabs/truss v0.18.7","date":"2026-06-09T21:42:48+00:00","date_source":"source","source_url":"https://github.com/basetenlabs/truss/releases/tag/v0.18.7","signal_url":"https://onlylabs.fyi/signals/438bd1d6-1b54-48af-a4de-f1005cad5e5e","signal_json_url":"https://onlylabs.fyi/signals/438bd1d6-1b54-48af-a4de-f1005cad5e5e/signal.json","text":"release · basetenlabs/truss v0.18.7 · signal_desk=releases · occurred_at=2026-06-09T21:42:48+00:00 · url=https://github.com/basetenlabs/truss/releases/tag/v0.18.7 · raw={\"repo\":\"basetenlabs/truss\"}"},{"ref":"E5","kind":"event","title":"Engineering Manager, Cloud Platform","date":"2026-06-09T18:23:27.319+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/baseten/0870ed34-7365-4b9f-a50a-481783b8c266","signal_url":"https://onlylabs.fyi/signals/456a26b8-1d15-4aa8-8ef9-5509ab3feac8","signal_json_url":"https://onlylabs.fyi/signals/456a26b8-1d15-4aa8-8ef9-5509ab3feac8/signal.json","text":"job_opened · Engineering Manager, Cloud Platform · signal_desk=hiring · occurred_at=2026-06-09T18:23:27.319+00:00 · url=https://jobs.ashbyhq.com/baseten/0870ed34-7365-4b9f-a50a-481783b8c266 · raw={\"location\":\"San Francisco\",\"team\":\"Infrastructure\",\"ats\":\"ashby\"}"},{"ref":"E6","kind":"event","title":"Engineering Manager, Internal Platform","date":"2026-06-09T18:12:27.736+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/baseten/1e721b74-58b9-4b03-ac0f-4b1e5c32342e","signal_url":"https://onlylabs.fyi/signals/66e5e498-c460-4c04-b4fa-6cd9bab21eda","signal_json_url":"https://onlylabs.fyi/signals/66e5e498-c460-4c04-b4fa-6cd9bab21eda/signal.json","text":"job_opened · Engineering Manager, Internal Platform · signal_desk=hiring · occurred_at=2026-06-09T18:12:27.736+00:00 · url=https://jobs.ashbyhq.com/baseten/1e721b74-58b9-4b03-ac0f-4b1e5c32342e · raw={\"location\":\"San Francisco\",\"team\":\"Internal Platform (Dev Tooling)\",\"ats\":\"ashby\"}"},{"ref":"E7","kind":"event","title":"Engineering Manager, Runtime Fabric","date":"2026-06-09T18:02:59.642+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/baseten/aae72bd3-6f75-4238-9741-95fec11facb9","signal_url":"https://onlylabs.fyi/signals/bdf68b9a-56a7-427a-9d90-5401af51c02b","signal_json_url":"https://onlylabs.fyi/signals/bdf68b9a-56a7-427a-9d90-5401af51c02b/signal.json","text":"job_opened · Engineering Manager, Runtime Fabric · signal_desk=hiring · occurred_at=2026-06-09T18:02:59.642+00:00 · url=https://jobs.ashbyhq.com/baseten/aae72bd3-6f75-4238-9741-95fec11facb9 · raw={\"location\":\"San Francisco\",\"team\":\"Runtime Fabric\",\"ats\":\"ashby\"}"},{"ref":"E8","kind":"event","title":"Strategic Finance Associate / Sr. Associate","date":"2026-06-08T18:26:02.827+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/baseten/71a011b6-0f17-4c0a-b0ba-38deffce1adb","signal_url":"https://onlylabs.fyi/signals/ad886de5-9ed0-420b-8fd8-5266d3eff536","signal_json_url":"https://onlylabs.fyi/signals/ad886de5-9ed0-420b-8fd8-5266d3eff536/signal.json","text":"job_opened · Strategic Finance Associate / Sr. Associate · signal_desk=hiring · occurred_at=2026-06-08T18:26:02.827+00:00 · url=https://jobs.ashbyhq.com/baseten/71a011b6-0f17-4c0a-b0ba-38deffce1adb · raw={\"location\":\"San Francisco\",\"team\":\"G&A\",\"ats\":\"ashby\"}"},{"ref":"E9","kind":"event","title":"basetenlabs/baseten-go v0.1.0","date":"2026-06-08T17:52:34+00:00","date_source":"source","source_url":"https://github.com/basetenlabs/baseten-go/releases/tag/v0.1.0","signal_url":"https://onlylabs.fyi/signals/1cebec52-c245-4db2-bce1-7839deb3391b","signal_json_url":"https://onlylabs.fyi/signals/1cebec52-c245-4db2-bce1-7839deb3391b/signal.json","text":"release · basetenlabs/baseten-go v0.1.0 · signal_desk=releases · occurred_at=2026-06-08T17:52:34+00:00 · url=https://github.com/basetenlabs/baseten-go/releases/tag/v0.1.0 · raw={\"repo\":\"basetenlabs/baseten-go\"}"},{"ref":"E10","kind":"event","title":"basetenlabs/baseten-python v0.9.0","date":"2026-06-08T14:33:19+00:00","date_source":"source","source_url":"https://github.com/basetenlabs/baseten-python/releases/tag/v0.9.0","signal_url":"https://onlylabs.fyi/signals/2c598b49-1126-4125-aaf2-5493dc18464d","signal_json_url":"https://onlylabs.fyi/signals/2c598b49-1126-4125-aaf2-5493dc18464d/signal.json","text":"release · basetenlabs/baseten-python v0.9.0 · signal_desk=releases · occurred_at=2026-06-08T14:33:19+00:00 · url=https://github.com/basetenlabs/baseten-python/releases/tag/v0.9.0 · raw={\"repo\":\"basetenlabs/baseten-python\"}"},{"ref":"E11","kind":"event","title":"basetenlabs/truss v0.18.6","date":"2026-06-05T21:56:46+00:00","date_source":"source","source_url":"https://github.com/basetenlabs/truss/releases/tag/v0.18.6","signal_url":"https://onlylabs.fyi/signals/30f87fbd-def8-4f72-9bde-e59e8210de31","signal_json_url":"https://onlylabs.fyi/signals/30f87fbd-def8-4f72-9bde-e59e8210de31/signal.json","text":"release · basetenlabs/truss v0.18.6 · signal_desk=releases · occurred_at=2026-06-05T21:56:46+00:00 · url=https://github.com/basetenlabs/truss/releases/tag/v0.18.6 · raw={\"repo\":\"basetenlabs/truss\"}"},{"ref":"E12","kind":"event","title":"basetenlabs/truss v0.18.5","date":"2026-06-04T16:20:11+00:00","date_source":"source","source_url":"https://github.com/basetenlabs/truss/releases/tag/v0.18.5","signal_url":"https://onlylabs.fyi/signals/4d27569e-cd7f-4d03-9e19-24c322d599b6","signal_json_url":"https://onlylabs.fyi/signals/4d27569e-cd7f-4d03-9e19-24c322d599b6/signal.json","text":"release · basetenlabs/truss v0.18.5 · signal_desk=releases · occurred_at=2026-06-04T16:20:11+00:00 · url=https://github.com/basetenlabs/truss/releases/tag/v0.18.5 · raw={\"repo\":\"basetenlabs/truss\"}"},{"ref":"E13","kind":"event","title":"basetenlabs/truss v0.18.5rc0","date":"2026-06-03T21:43:14+00:00","date_source":"source","source_url":"https://github.com/basetenlabs/truss/releases/tag/v0.18.5rc0","signal_url":"https://onlylabs.fyi/signals/87a340e3-ad61-474d-abe6-c120a7d3d460","signal_json_url":"https://onlylabs.fyi/signals/87a340e3-ad61-474d-abe6-c120a7d3d460/signal.json","text":"release · basetenlabs/truss v0.18.5rc0 · signal_desk=releases · occurred_at=2026-06-03T21:43:14+00:00 · url=https://github.com/basetenlabs/truss/releases/tag/v0.18.5rc0 · raw={\"repo\":\"basetenlabs/truss\"}"},{"ref":"E14","kind":"event","title":"basetenlabs/ideogram4","date":"2026-06-03T20:18:15+00:00","date_source":"source","source_url":"https://github.com/basetenlabs/ideogram4","signal_url":"https://onlylabs.fyi/signals/2c538801-4357-4519-a15a-708e2ece2643","signal_json_url":"https://onlylabs.fyi/signals/2c538801-4357-4519-a15a-708e2ece2643/signal.json","text":"repo_forked · basetenlabs/ideogram4 · signal_desk=forks · occurred_at=2026-06-03T20:18:15+00:00 · url=https://github.com/basetenlabs/ideogram4 · raw={\"repo\":\"basetenlabs/ideogram4\",\"parent\":\"ideogram-oss/ideogram4\"}"},{"ref":"E15","kind":"event","title":"basetenlabs/tinker-cookbook","date":"2026-06-03T01:38:21+00:00","date_source":"source","source_url":"https://github.com/basetenlabs/tinker-cookbook","signal_url":"https://onlylabs.fyi/signals/fd7262b9-0c91-4cb4-8988-e408d16e0f67","signal_json_url":"https://onlylabs.fyi/signals/fd7262b9-0c91-4cb4-8988-e408d16e0f67/signal.json","text":"repo_forked · basetenlabs/tinker-cookbook · signal_desk=forks · occurred_at=2026-06-03T01:38:21+00:00 · url=https://github.com/basetenlabs/tinker-cookbook · stars=1 · raw={\"repo\":\"basetenlabs/tinker-cookbook\",\"parent\":\"thinking-machines-lab/tinker-cookbook\"}"},{"ref":"E16","kind":"event","title":"basetenlabs/truss 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Counsel, Infrastructure & Compute · signal_desk=hiring · occurred_at=2026-06-02T21:44:41.553+00:00 · url=https://jobs.ashbyhq.com/baseten/0cf1410f-39ca-47b5-9fc3-8e75bd9c1e0e · raw={\"location\":\"San Francisco\",\"team\":\"Legal\",\"ats\":\"ashby\"}"},{"ref":"E18","kind":"event","title":"Head of Legal Operations","date":"2026-06-02T21:42:11.605+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/baseten/f5077169-a944-41ca-a851-878b146811b6","signal_url":"https://onlylabs.fyi/signals/21dece36-5b0d-40ad-bf52-e17ec277d40f","signal_json_url":"https://onlylabs.fyi/signals/21dece36-5b0d-40ad-bf52-e17ec277d40f/signal.json","text":"job_opened · Head of Legal Operations · signal_desk=hiring · occurred_at=2026-06-02T21:42:11.605+00:00 · url=https://jobs.ashbyhq.com/baseten/f5077169-a944-41ca-a851-878b146811b6 · raw={\"location\":\"San Francisco\",\"team\":\"Legal\",\"ats\":\"ashby\"}"},{"ref":"E19","kind":"event","title":"Assistant General Counsel, 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signal_desk=forks · occurred_at=2026-06-02T16:22:10+00:00 · url=https://github.com/basetenlabs/runc · raw={\"repo\":\"basetenlabs/runc\",\"parent\":\"opencontainers/runc\"}"},{"ref":"E23","kind":"event","title":"Software Engineer- BIS (Baseten Inference Stack)","date":"2026-06-02T00:16:06.843+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/baseten/c8701794-bdc1-4932-bffa-a444ce57ed73","signal_url":"https://onlylabs.fyi/signals/01ab8703-deb2-4b22-94a6-32a2e82ecbcd","signal_json_url":"https://onlylabs.fyi/signals/01ab8703-deb2-4b22-94a6-32a2e82ecbcd/signal.json","text":"job_opened · Software Engineer- BIS (Baseten Inference Stack) · signal_desk=hiring · occurred_at=2026-06-02T00:16:06.843+00:00 · url=https://jobs.ashbyhq.com/baseten/c8701794-bdc1-4932-bffa-a444ce57ed73 · raw={\"location\":\"San Francisco\",\"team\":\"Engineering\",\"ats\":\"ashby\"}"},{"ref":"E24","kind":"event","title":"basetenlabs/truss 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occurred_at=2026-05-22T19:50:40+00:00 · url=https://github.com/basetenlabs/baseten-cli/releases/tag/v0.1.0 · raw={\"repo\":\"basetenlabs/baseten-cli\"}"},{"ref":"E26","kind":"event","title":"Data Center Network Engineer","date":"2026-05-21T22:17:40.355+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/baseten/8570f34d-edfe-4bc2-b2b1-eda37155eddb","signal_url":"https://onlylabs.fyi/signals/ebc9c4eb-e5e8-4328-bb71-2748a68a1b2b","signal_json_url":"https://onlylabs.fyi/signals/ebc9c4eb-e5e8-4328-bb71-2748a68a1b2b/signal.json","text":"job_opened · Data Center Network Engineer · signal_desk=hiring · occurred_at=2026-05-21T22:17:40.355+00:00 · url=https://jobs.ashbyhq.com/baseten/8570f34d-edfe-4bc2-b2b1-eda37155eddb · raw={\"location\":\"San Francisco\",\"team\":\"Hardware 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Recruiter","date":"2026-05-18T14:14:40.542+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/baseten/431674b6-ee6b-495e-a30f-edc660a96b62","signal_url":"https://onlylabs.fyi/signals/4a840239-4fe4-4d18-ad02-b7b1af103672","signal_json_url":"https://onlylabs.fyi/signals/4a840239-4fe4-4d18-ad02-b7b1af103672/signal.json","text":"job_opened ·  Executive Recruiter · signal_desk=hiring · occurred_at=2026-05-18T14:14:40.542+00:00 · url=https://jobs.ashbyhq.com/baseten/431674b6-ee6b-495e-a30f-edc660a96b62 · raw={\"location\":\"San Francisco\",\"team\":\"Talent\",\"ats\":\"ashby\"}"},{"ref":"E30","kind":"event","title":"Recruiting Operations Lead","date":"2026-05-18T14:11:16.118+00:00","date_source":"source","source_url":"https://jobs.ashbyhq.com/baseten/8d362149-b684-43b2-a180-d2ae4ae2dfc2","signal_url":"https://onlylabs.fyi/signals/2e69371b-2d57-4b18-b5b0-e1df1de532b8","signal_json_url":"https://onlylabs.fyi/signals/2e69371b-2d57-4b18-b5b0-e1df1de532b8/signal.json","text":"job_opened · Recruiting Operations Lead · signal_desk=hiring · occurred_at=2026-05-18T14:11:16.118+00:00 · url=https://jobs.ashbyhq.com/baseten/8d362149-b684-43b2-a180-d2ae4ae2dfc2 · raw={\"location\":\"San Francisco\",\"team\":\"Talent\",\"ats\":\"ashby\"}"},{"ref":"E31","kind":"event","title":"Engineering Manager, Cloud Platform","date":"2026-05-17T21:30:53.701+00:00","date_source":"source","source_url":"https://jobs.ashbyhq.com/baseten/1933097e-2f8e-46e2-8442-0c2f4a9394da","signal_url":"https://onlylabs.fyi/signals/98408cb0-f296-49aa-b37b-f1557efbe59d","signal_json_url":"https://onlylabs.fyi/signals/98408cb0-f296-49aa-b37b-f1557efbe59d/signal.json","text":"job_opened · Engineering Manager, Cloud Platform · signal_desk=hiring · occurred_at=2026-05-17T21:30:53.701+00:00 · url=https://jobs.ashbyhq.com/baseten/1933097e-2f8e-46e2-8442-0c2f4a9394da · raw={\"location\":\"San Francisco\",\"team\":\"Infrastructure\",\"ats\":\"ashby\"}"},{"ref":"E32","kind":"event","title":"Senior Manager, Cloud Platform & Site 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· SRE · signal_desk=hiring · occurred_at=2026-05-11T21:33:35.139+00:00 · url=https://jobs.ashbyhq.com/baseten/ff008b8e-b38d-4941-b24f-9a48c970e7fb · raw={\"location\":\"San Francisco\",\"team\":\"Infrastructure\",\"ats\":\"ashby\"}"},{"ref":"E37","kind":"event","title":"Solution Architect (AI/LLM Inference)","date":"2026-05-10T18:44:44.405+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/baseten/54c83823-574e-40ad-9cff-a57acff0ffe6","signal_url":"https://onlylabs.fyi/signals/444e3224-2360-431a-8667-0f2ad1d98492","signal_json_url":"https://onlylabs.fyi/signals/444e3224-2360-431a-8667-0f2ad1d98492/signal.json","text":"job_opened · Solution Architect (AI/LLM Inference) · signal_desk=hiring · occurred_at=2026-05-10T18:44:44.405+00:00 · url=https://jobs.ashbyhq.com/baseten/54c83823-574e-40ad-9cff-a57acff0ffe6 · raw={\"location\":\"San Francisco\",\"team\":\"Forward Deployed Engineering\",\"ats\":\"ashby\"}"},{"ref":"E38","kind":"event","title":"Engineering 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basetenlabs/tmp-animations · signal_desk=repos · occurred_at=2026-04-20T18:52:35+00:00 · url=https://github.com/basetenlabs/tmp-animations · raw={\"repo\":\"basetenlabs/tmp-animations\",\"description\":\"a tiny gh pages for misc documents\",\"language\":\"HTML\"}"},{"ref":"E51","kind":"event","title":"Integrated Marketing Manager","date":"2026-04-17T22:40:17.371+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/baseten/3bd6f288-8ba7-4486-8ac4-7e57b44040df","signal_url":"https://onlylabs.fyi/signals/037c5119-40d1-4e16-93ed-947c128c215b","signal_json_url":"https://onlylabs.fyi/signals/037c5119-40d1-4e16-93ed-947c128c215b/signal.json","text":"job_opened · Integrated Marketing Manager · signal_desk=hiring · occurred_at=2026-04-17T22:40:17.371+00:00 · url=https://jobs.ashbyhq.com/baseten/3bd6f288-8ba7-4486-8ac4-7e57b44040df · raw={\"location\":\"San 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basetenlabs/goproxy · signal_desk=forks · occurred_at=2026-03-17T20:45:34+00:00 · url=https://github.com/basetenlabs/goproxy · raw={\"repo\":\"basetenlabs/goproxy\",\"parent\":\"elazarl/goproxy\"}"}]}