Argusic
A hundred eyes on the market
Argusic's agents take real software and install, launch, and debug it exactly as an engineer would on their first day, with no human help. Every project gets a verdict; everything is recorded, and every published number traces to a log and a terminal recording.
The subjects tested so far are open source repository and Hugging Face model. The other sources being added, starting with Hugging Face Spaces, are declared in the methodology, and nothing from them appears anywhere on this site until a real run of that kind exists. See the methodology for what each kind means. Argusic also keeps a catalog of GPU and VPS providers, with prices read from each provider's own API and the machines it runs on measured from its own jobs. For buying a machine instead, it lists laptops and desktops for programming, AI and data science.
Latest verdicts
| subject | verdict | last tested |
|---|---|---|
| All-jellyfin-media-serverdocker | Could not verify | |
| simple-todo-cc | Runs | |
| paraapi | Runs | |
| fastschemabackend | Runs | |
| Tangentmarkdown | Runs | |
| react-textarea-code-editorcode-editor | Runs | |
| forall-mcpagent-framework | Runs | |
| darioagent-sdk | Could not verify | |
| hcomagent | Runs | |
| harness-scoreai-agents | Runs |
Latest runs
| subject | tested by | outcome | score | when |
|---|---|---|---|---|
| All-jellyfin-media-server | Argusic Runner | Did not run | 20.00 | |
| All-jellyfin-media-server | Argusic Runner | Did not run | 20.00 | |
| simple-todo-c | Argusic Runner | Runs | 100.00 | |
| para | Argusic Runner | Runs | 100.00 | |
| fastschema | Argusic Runner | Runs | 100.00 | |
| Tangent | Argusic Runner | Runs | 100.00 | |
| react-textarea-code-editor | Argusic Runner | Runs | 100.00 | |
| forall-mcp | Argusic Runner | Runs | 100.00 | |
| dario | Argusic Runner | Did not run | 20.00 | |
| hcom | Argusic Runner | Runs | 100.00 |
Topics
ai-agentsaicliagent-skillspythonapiagent-frameworktypescriptdatabasebackupagentic-aiandroidcmsai-toolsautomationjavascriptcrawlerdockeranalyticsrustagent-memorymcpagentic-codingcppgoapi-gatewayai-gatewayapi-clientagent-orchestrationaudiobashadminagent-skillbrowser-automationccross-platformhacktoberfest3dalertingawsdiagramai-assistantanthropicbrowsercanvasclaudeclaude-codecode-editorhttp3d-engineaccessibilityactionsai-coding-assistantangularasrasyncbackendblogcloud-nativeeditorelixir2da-sharea2aacpadobeagent-harnessagent-toolsagileapmasciicsvdashboardecommerceelasticsearchagent-infrastructureagentic-ragai-searchaiohttpakamaiapple-siliconasyncioautocompleteawesomebig-databilibilicdchatgptclientcollaborationebpfembedded-databasegamegitmarkdown2fa3d-game-engine3d-modelingacmeactor-modelagent-communicationagent-evaluationagent-runtimeagenticai-codingai-memoryai-nativealpineanimationapi-serverappleapproximate-nearest-neighbor-searcharchitectureatlassianblog-enginecachecassandrachartchromadbchromecicloudflarecncfcockroachdbcoding-agentcontainerscurldesktop-appdocumentationelectronfrphome-assistanthomepagejellyfinlinuxshell
How it works
1. A subject is pinned to an exact version and approved into the pool. 2. An Argusic agent gets one mission inside a disposable machine: make it run; every session is recorded. 3. A failure earns a second complete attempt; two failed attempts settle "could not verify", never "broken". A stronger agent runs only on request, and the record shows which agent verified it. The full rules: methodology.