Search Results for "estimates"
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Showing 194 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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$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
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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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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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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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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. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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5
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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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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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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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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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". -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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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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MEV Arbitrage Bot for Ethereum
Arbitrage bot is a smart contract connected to an external script
...Owners can whitelist tokens and routers, configure fees and swap limits, pause execution, revoke approvals, and withdraw assets. The automation layer performs dry-run gas estimates before sending eligible transactions. It also monitors live Uniswap V2 and V3 swap events and displays activity in real time. The project is intended to automate on-chain arbitrage, although actual profitability depends on competition, volatility, liquidity, and gas costs. -
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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. -
13
Clawbolt
The AI Assistant that actually does things for the trades
Clawbolt is an open-source messaging-first AI assistant built specifically for contractors, tradespeople, and service businesses that prefer managing work through chat instead of traditional dashboards. The platform allows users to interact with an AI assistant through iMessage, SMS, RCS, Telegram, and related messaging channels to handle tasks such as estimates, invoices, scheduling, reminders, and client communication. Clawbolt combines large language model orchestration with memory systems, file storage integrations, and tool-calling workflows to create an assistant capable of managing real operational tasks instead of only answering prompts. The project supports integrations with QuickBooks Online, Google Calendar, Dropbox, and Google Drive, enabling automated business workflows tied directly to conversations. ... -
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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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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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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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Napkin Math
Techniques and numbers for estimating system's performance
Napkin Math is a technical reference project for estimating software system performance from first principles. It collects practical numbers, benchmark-style measurements, and mental models that help engineers make fast back-of-the-envelope calculations. The project is useful for questions like how much memory throughput matters, how long storage operations may take, what network latency to expect, or how expensive logging could become at high request volume. It treats these values as... -
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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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iMCP
A macOS app that provides an MCP server to your Messages
...A bundled command-line server communicates over standard input and output with clients such as Claude Desktop, Claude Code, Cursor, and Amp. Calendar and reminder tools can create items, while other tools support searches, directions, travel estimates, message history, and current conditions. The app itself does not collect or store user data, although connected AI clients may send tool results off device. It requires macOS 15.3 or later and can be installed directly or through Homebrew. -
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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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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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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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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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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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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.
