Papers with Code VS Cachely.dev

Compare Papers with Code VS Cachely.dev and see what are their differences

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Papers with Code

The latest in machine learning at your fingerprints

Cachely.dev

Cachely is a managed implementation of self-hosted remote cache for monorepos. Speed up CI, prove how much time and cost you saved, get build optimization suggestions, safe from cache poisoning (CVE-2025-36852). Turborepo and Bazel on the roadmap.

Landing page //
2022-07-17

Landing page //
2026-08-01

Cachely is the managed self-hosted remote cache for Nx and Turborepo - the cache backend you'd otherwise build and run yourself, hosted for you on Cloudflare's edge (R2). It's a drop-in replacement for a DIY @nx/s3-cache / S3 bucket setup: point your build tool at Cachely with a token and two environment variables, and share build cache across CI and every developer's laptop.

Unlike a self-hosted cache, Cachely enforces read-only tokens at the API, so pull-request and fork builds can read but never write - closing the Nx cache-poisoning attack (CVE-2025-36852). It adds ROI reporting (the real build minutes and dollars the cache saved), per-tool insights, and build-optimization suggestions on top.

Pricing is a flat per-workspace subscription with no per-seat fees - add every developer, bot, and CI actor without watching the bill. Cachely never stores your source code; it caches only task outputs and their content hashes. Nx and Turborepo today; Bazel on the roadmap.

Papers with Code

Website: paperswithcode.com
Pricing URL: -
$ Details: -
Release Date: -

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Cachely.dev

Website: cachely.dev
Pricing URL: Official Cachely.dev Pricing
$ Details: freemium
Release Date: 2026 June

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Papers with Code features and specs

Open Access
Papers with Code provides free access to a vast repository of research papers and code implementations, making cutting-edge research available to a wider audience.
Reproducibility
By linking research papers with their corresponding code, it promotes reproducibility, allowing researchers to verify results and build upon previous work more effectively.
Benchmarking
The platform offers benchmarking tools and leaderboards, facilitating the comparison of different models and approaches on standard datasets and fostering competition in the research community.
Community Engagement
Researchers and developers can contribute their own code and evaluations, which encourages community collaboration and the sharing of knowledge.
Resource Saving
By providing implementations and datasets, it saves researchers time and resources, enabling them to focus on innovation rather than recreating existing work.

Possible disadvantages of Papers with Code

Quality Control
Not all code implementations are thoroughly vetted or peer-reviewed, which can lead to issues with code quality and reliability.
Misalignment of Benchmarks
Benchmarks and evaluations might not perfectly align with certain niche or novel research tasks, potentially skewing perceptions about model performance.
Dependence on Contributor Participation
The platform relies heavily on community contributions; if participation wanes, the updates and breadth of resources could stagnate.
Integration Challenges
Integrating and adapting third-party code into different environments or existing projects can sometimes be challenging due to dependencies or compatibility issues.
Information Overload
With a vast amount of available papers and code, navigating and finding the most relevant and high-quality resources can be overwhelming for users.

Cachely.dev features and specs

Simplified Caching Setup
Cachely.dev likely offers an easy-to-integrate caching layer that reduces the complexity of manually configuring caching infrastructure, allowing developers to implement caching with minimal setup time.
Performance Improvement
By providing a dedicated caching solution, Cachely.dev can help reduce latency and improve application response times, especially for frequently accessed data or API responses.
Developer-Focused Design
The .dev domain and branding suggest the product is tailored specifically for developers, potentially offering clean APIs, SDKs, and documentation that fit into modern development workflows.
Scalability
As a specialized caching service, it may be built to handle scaling automatically, removing the burden of managing cache infrastructure as traffic grows.
Reduced Backend Load
Effective caching can significantly reduce the load on primary databases and backend services, potentially lowering infrastructure costs and improving overall system reliability.

Papers with Code videos

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The best site for research papers with codes on Machine/Deep Learning | Research paper search

Cachely.dev videos

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Category Popularity

0-100% (relative to Papers with Code and Cachely.dev)

Papers with Code

Cachely.dev

100 100%

0% 0

Productivity

71 71%

Productivity

29% 29

Developer Tools

87 87%

Developer Tools

13% 13

Data Science And Machine Learning

100 100%

Data Science And Machine Learning

0% 0

User comments

Share your experience with using Papers with Code and Cachely.dev. For example, how are they different and which one is better?

Social recommendations and mentions

Based on our record, Papers with Code seems to be more popular. It has been mentiond 100 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Papers with Code mentions (100)

What does HumaneBench AI benchmark reveal about chatbot safety?
Benchmark Primary focus Evaluation metrics System coverage Usability Link HumaneBench AI benchmark Human well being, humane AI principles HumaneScore, flip tests under adversarial instruction, long term well being 15 popular chat models tested across 800 realistic scenarios Designed for chatbot safety research; requires ensemble judging for... - Source: dev.to / 9 months ago
Computer Vision Made Simple with ReductStore and Roboflow
An helpful approach is to browse the state of the art models in paperswithcode. This will give you an idea of the performance of different models on various tasks. - Source: dev.to / almost 2 years ago
Show HN: Simple Science – The Newest Science Explained Simply
I think a way around this would some sort of voting/ popularity system? Papers with code (https://paperswithcode.com/) does this via Github stars sorting. Sure it doesn't mean something is established. But it at least gives some way to filter through the firehose of papers. Love this project btw! I think it has potential (and the timing is right now that everyone is looking for the next "attention is all... - Source: Hacker News / about 2 years ago
Essential Deep Learning Checklist: Best Practices Unveiled
Adapting to Evolving Standards: With the rapid progress in deep learning research and applications, staying current with the latest developments is crucial. The checklist underscores the importance of considering established standard architectures and leveraging current state-of-the-art (SOTA) resources, like paperswithcode.com, to guide project decisions. This dynamic approach ensures that projects benefit from... - Source: dev.to / about 2 years ago
Understanding Technical Research Papers
Papers With Code is one of the good resources to get you to get started. - Source: dev.to / over 2 years ago