ArangoDB Reviews

GitHub Reviews

1. Reinhard

   · Boss at CLOUD Meister · over 5 years ago
   perfect 4 open Source

ArangoDB mentions (6)

• MongoDB takes a swing at PostgreSQL after claiming wins against rival

  If you like the document db idea there are a lot of choices, especially https://arangodb.com/ which I think gets little attention because people who use it see it as a secret weapon. Too bad about the license though. Also https://couchdb.apache.org/ and https://developer.marklogic.com/. - Source: Hacker News / almost 2 years ago

• Which Database is Perfect for You? A Comprehensive Guide to MySQL, PostgreSQL, NoSQL, and More

  ArangoDB is a multi-model database that supports document, key-value, and graph data models with a unified query language. - Source: dev.to / about 2 years ago

• Demonstrating ArangoDB VelocyPack: A High-Performance Binary Data Format

  In modern databases, efficient data serialization and deserialization are paramount to achieving high performance. ArangoDB, a multi-model database, addresses this need with its innovative binary data format, VelocyPack. This article delves into the intricacies of VelocyPack, demonstrating its advantages, usage, and how it enhances the performance of ArangoDB with code examples in Java and Rust. - Source: dev.to / about 2 years ago

• How to choose the right type of database

  ArangoDB: A native multi-model database, it offers flexibility for documents, graphs, and key-values. This versatility makes it suitable for applications requiring a combination of these data models. - Source: dev.to / over 2 years ago

• Spidergram is a collection of tools my company Autogram has built or enabled over the past several years to support our work to automate content inventories for large websites: it's part web crawler, part domain model, and part mad science. We released the first public beta today.

  ArangoDB, a "multi-modal" database engine that stores arbitrary JSON documents like MongoDB, key/value data like Redis, and graph relationships like Neo4j — and lets you leverage all three kinds of data in a single query. Source: over 3 years ago

GitHub mentions (2474)

• I audited a 5.5 GB AI training dataset by downloading 0.8% of it

  Import struct, json, urllib.request REL = "https://github.com/{owner}/{repo}/releases/download/{tag}/" PART = ["...part1.zip.001", "...part2.zip.002", "...part3.zip.003"] SIZE = [1992294400, 1992294400, 1893639808] # from the releases API Def grab(part, start, end, out): # HTTP range fetch req = urllib.request.Request(REL + PART[part], Headers={"Range":... - Source: dev.to / 4 days ago

• Superlogical – Mitchell Hashimoto

  Is published at https://github.com/.keys so an SSH server to which you connect could do a reverse lookup. This is the reason why my ~/.ssh/config has those 2 lines at the end:

          Host *.

  - Source: Hacker News / 11 days ago

• Your Agent's Confidence Score Is Not a Probability

  All of this assumes you can actually inspect what the agent did — the real inputs after resolution, the real tool outputs, the real intermediate steps. That is the other half of the workflow. AgentLens captures the trace: every model and tool step, resolved inputs, raw outputs. agent-eval scores and gates the output; AgentLens gives you the unforgeable, agent-didn't-author trace data for Tier 1+2 to score against... - Source: dev.to / 12 days ago

• Foreman 101: agentic coding as Kubernetes resources

  # git: the API token, plus the credential used for the push Kubectl create secret generic foreman-github \ --from-literal=GITHUB_TOKEN="$GITHUB_TOKEN" -n foreman-system Kubectl create secret generic foreman-git-credentials \ --from-literal=token="$GITHUB_TOKEN" -n foreman-system Helm upgrade foreman llmkube/foreman -n foreman-system --reuse-values \ --set agent.githubToken.secretName=foreman-github \ ... - Source: dev.to / 12 days ago

• Stop Judging Every Run: Eval Sampling Is a Budget Decision, Not a Coverage One

  This is why eval and observability ship as a unit, not as separate purchases. agent-eval scores and gates the output — the tiers above, drift, hallucination. AgentLens captures the trace of how the agent got there: every model step and tool call, the resolved inputs, the raw outputs, the trajectory. Two things fall out of that:. - Source: dev.to / 22 days ago