Search Results for "estimates"
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Showing 186 open source projects for "estimates"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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Infracost VSCode Extension
See cost estimates for Terraform right in your editor
Infracost's VSCode extension shows you cost estimates for Terraform right in your editor! Prevent costly infrastructure changes before they get into production. Compare configs, instance types, regions etc: copy/paste a code block, make changes and compare them. Quick cost estimate: write a code block and get a cost estimate without having to use AWS, Azure or Google cost calculators, or read the long/complicated pricing web pages. -
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SolidInvoice
Simple and elegant invoicing solution
...It helps you manage clients, contacts, day-to-day billing and more so you can always get paid right and on time. SolidInvoice is capable of handling recurring invoices, quotes and estimates, as well as online payments. You can receive payments from a number of different gateways such as Paypal Express, Stripe Checkout, Be2bill and more. It also supports multiple currencies. SolidInvoice has a powerful and flexible RESTful API so you can easily integrate it to any other service. -
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Infracost
Cloud cost estimates for Terraform in pull requests
...Infracost integrates with Open Policy Agent, Sentinel, and Conftest, enabling DevOps teams to set best practices as policies. Infracost automatically creates detailed, shareable cost estimates which can be sent to clients and managers with different scenarios. Infracost supports over 230 Terraform resources across AWS, Azure and Google. Other IaC tools, such as Pulumi, AWS CloudFormation/CDK and Azure ARM/Bicep are on our roadmap. Infracost can also estimate usage-based resources such as AWS S3 or Lambda! Infracost has many CI/CD integrations so you can easily post cost estimates in pull requests. ... -
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ConformalPrediction.jl
Predictive Uncertainty Quantification through Conformal Prediction
...Conformal Prediction is easy-to-understand, easy-to-use and model-agnostic and it works under minimal distributional assumptions. Intuitively, CP works under the premise of turning heuristic notions of uncertainty into rigorous uncertainty estimates through repeated sampling or the use of dedicated calibration data. -
Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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PEV2
Postgres Explain Visualizer 2
PEV2 is a web-based tool to visualize PostgreSQL execution plans. It takes EXPLAIN or EXPLAIN ANALYZE output and displays it in an interactive, tree-like format that highlights performance metrics, helping users understand query behavior and bottlenecks. PEV2 is a complete rewrite of the original PEV, offering a modern, user-friendly interface for query optimization. -
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Kalshi Trading Bot CLI
AI-native CLI for trading Kalshi prediction markets
Kalshi Trading Bot CLI is an AI-driven command-line tool designed to automate trading strategies on Kalshi prediction markets by combining quantitative modeling with real-time market data. It operates by conducting deep research on events, generating independent probability estimates, and comparing those estimates against current market prices to identify trading opportunities. The system incorporates advanced decision-making logic, including Kelly criterion-based position sizing and a structured multi-step risk evaluation process before executing trades. Built as a CLI application, it allows traders to interact programmatically with markets, making it suitable for automation and integration into larger trading pipelines. ... -
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Lasso.jl
Lasso/Elastic Net linear and generalized linear models
Lasso.jl is a pure Julia implementation of the glmnet coordinate descent algorithm for fitting linear and generalized linear Lasso and Elastic Net models. -
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llmfit
157 models, 30 providers, one command to find what runs on hardware
...It provides both an interactive terminal user interface and a traditional CLI mode, enabling flexible workflows for different user preferences. llmfit also supports advanced configurations including multi-GPU setups, mixture-of-experts architectures, and dynamic quantization recommendations. By presenting clear performance estimates and compatibility guidance, the project reduces the trial-and-error typically involved in local LLM experimentation. Overall, llmfit serves as a practical decision assistant for developers who want to run language models efficiently on their own machines. -
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FiniteDifferences.jl
High accuracy derivatives, estimated via numerical finite differences
FiniteDifferences.jl estimates derivatives with finite differences. See also the Python package FDM. FiniteDiff.jl and FiniteDifferences.jl are similar libraries: both calculate approximate derivatives numerically. You should definitely use one or the other, rather than the legacy Calculus.jl finite differencing, or reimplementing it yourself. At some point in the future, they might merge, or one might depend on the other. -
Application Monitoring That Won't Slow Your App DownFull APM with errors, performance, logs, and uptime monitoring. 99.999% uptime SLA on the platform itself.
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Causal ML
Uplift modeling and causal inference with machine learning algorithms
...It provides a standard interface that allows users to estimate the Conditional Average Treatment Effect (CATE) or Individual Treatment Effect (ITE) from experimental or observational data. Essentially, it estimates the causal impact of intervention T on outcome Y for users with observed features X, without strong assumptions on the model form. An important lever to increase ROI in an advertising campaign is to target the ad to the set of customers who will have a favorable response in a given KPI such as engagement or sales. CATE identifies these customers by estimating the effect of the KPI from ad exposure at the individual level from A/B experiments or historical observational data. -
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MediaPipe
Cross-platform, customizable ML solutions for live and streaming media
MediaPipe offers open-source cross-platform, customizable ML solutions for live and streaming media. Provides segmentation masks for prominent humans in the scene. MediaPipe Face Mesh is a face geometry solution that estimates 468 3D face landmarks in real-time even on mobile devices. It employs machine learning (ML) to infer the 3D surface geometry, requiring only a single camera input without the need for a dedicated depth sensor. Utilizing lightweight model architectures together with GPU acceleration throughout the pipeline, the solution delivers real-time performance-critical for live experiences. ... -
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ShinyItemAnalysis
Test and Item Analysis via Shiny
...All methods include sample R code which is ready to copy and paste into R and run locally. Several toy data sets are ready to use. You can also upload and analyze your own data. ShinyItemAnalysis provides model equations, parameter estimates and their interpretation. -
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Return YouTube Dislike
Chrome extension to return youtube dislikes
...Additionally, the dislike field in the YouTube API was removed on December 13th, 2021, removing any ability to judge the quality of content before watching. With the removal of dislike stats from the YouTube API, our backend switched to using a combination of scraped dislike stats, and estimates extrapolated from extension user data. None existing YouTube ID will return status code 404 "Not Found". Wrong formed YouTube ID will return 400 "Bad Request". -
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TokenCost
Easy token price estimates for 400+ LLMs. TokenOps
TokenCost is an open-source developer utility designed to estimate the cost of using large language model APIs by calculating token usage and translating it into real monetary values. The tool focuses on helping developers understand how much their prompts and generated completions cost when interacting with commercial AI models. It works by counting tokens in prompts and responses before or after sending requests and then applying pricing information associated with different models. This... -
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BenchmarkTools.jl
A benchmarking framework for the Julia language
BenchmarkTools makes performance tracking of Julia code easy by supplying a framework for writing and running groups of benchmarks as well as comparing benchmark results. This package is used to write and run the benchmarks found in BaseBenchmarks.jl. The CI infrastructure for automated performance testing of the Julia language is not in this package but can be found in Nanosoldier.jl. Our story begins with two packages, "Benchmarks" and "BenchmarkTrackers". The Benchmarks package... -
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Sloc Cloc and Code (scc)
Sloc, Cloc and Code: scc is a very fast accurate code counter
Sloc, Cloc and Code: scc is a very fast accurate code counter with complexity calculations and COCOMO estimates written in pure Go. The tool is similar to cloc, sloccount and tokei. For counting the lines of code, blank lines, comment lines, and physical lines of source code in many programming languages. The goal is to be the fastest code counter possible, but also perform COCOMO calculations like sloccount, estimate code complexity similar to cyclomatic complexity calculators, and produce unique lines of code or DRYness metrics. ... -
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hyperfine
A command-line benchmarking tool
A command-line benchmarking tool. Statistical analysis across multiple runs. Support for arbitrary shell commands. Constant feedback about the benchmark progress and current estimates. Warmup runs can be executed before the actual benchmark. Cache-clearing commands can be set up before each timing run. Statistical outlier detection to detect interference from other programs and caching effects. Export results to various formats: CSV, JSON, Markdown, AsciiDoc. Parameterized benchmarks (e.g. vary the number of threads). ... -
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DeepLabCut
Implementation of DeepLabCut
...We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. The package is open source, fast, robust, and can be used to compute 3D pose estimates or for multi-animals. Please see the original paper and the latest work below! This package is collaboratively developed by the Mathis Group & Mathis Lab at EPFL (releases prior to 2.1.9 were developed at Harvard University). The code is freely available and easy to install in a few clicks with Anaconda (and pypi). DeepLabCut is an open-source Python package for animal pose estimation. -
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AWS IoT FleetWise Edge
AWS IoT FleetWise Edge Agent
...Train autonomous vehicles (AVs) and advanced driver assistance systems (ADAS) with camera data collected from a fleet of production vehicles. Improve electric vehicle (EV) battery range estimates with crowdsourced environmental data, such as weather and driving conditions, from nearby vehicles. Collect select data from nearby vehicles and use it to notify drivers of changing road conditions, such as lane closures or construction. Use near real-time data to proactively detect and mitigate fleet-wide quality issues. -
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NannyML
Detecting silent model failure. NannyML estimates performance
NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, and interactive visualizations, is completely model-agnostic, and currently supports all tabular classification use cases. NannyML closes the loop with performance monitoring and post deployment data... -
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Claude Code Usage Monitor
Real-time Claude Code usage monitor with predictions and warnings
...It presents analytics through a visually rich terminal interface built with modern Python tooling, making it easy to interpret usage trends at a glance. The system includes predictive logic that estimates whether a session is likely to exceed limits before completion, allowing proactive adjustments to workflows. Its architecture emphasizes modularity and extensibility, supporting multiple Claude plan configurations and customizable monitoring behavior. Overall, the tool fills an important observability gap for heavy Claude Code users who need precise, local insight into AI usage economics and session management. -
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TTRL
Test-Time Reinforcement Learning
...The project addresses the problem of how to generate useful reward signals from unlabeled test-time data, and its central insight is that common test-time scaling practices such as majority voting can be repurposed into reward estimates for online reinforcement learning. This makes the framework especially interesting for scenarios where models must keep adapting during evaluation or deployment instead of relying only on fixed pretraining and static fine-tuning. The repository is implemented on top of the verl ecosystem, which allows users to enable TTRL as part of an existing reinforcement learning workflow rather than building a new stack from scratch. -
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TimesFM
Pretrained time-series foundation model developed by Google Research
...The project includes code and an inference API intended to make it practical to run forecasts programmatically, with options to use different backends such as Torch or Flax depending on your environment and performance needs. Newer releases emphasize expanded context handling and more flexible forecasting outputs, including quantile forecasting so users can get uncertainty estimates rather than only point predictions. The repository also documents how model versions evolved, with newer variants focusing on efficiency and longer context windows while maintaining forecasting quality. -
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see
Visualisation toolbox for beautiful and publication-ready figures
see is an R package that serves as the visualization component of the easystats ecosystem, providing plotting utilities to produce publication-ready visualizations of statistical model parameters, diagnostics, predictions, and performance metrics. It works in conjunction with other easystats packages (such as parameters, performance, modelbased, bayestestR, etc.) to convert model outputs or summary objects into visual forms (dot-and-whisker plots, diagnostic plots, residual plots, etc.). It... -
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BACE for gretl
Bayesian Averaging of Classical Estimates
Bayesian Averaging of Classical Estimates package.