Injester’s hackathon prototype used Tavily to extract website content and Nebius models to restructure it for AI agents. Its repository identifies Tavily Extract API and Nebius Token Factory, with an iterative prompt optimization loop and benchmarks comparing raw and restructured pages. Benjamin Shyong reports that switching from Llama 70B to Nemotron 3 Super and further optimization reduced the full workflow from nearly five minutes to 90 seconds.
Injester also powers LaClawClaw, an agent-oriented Shopify store. That product’s technical page describes Tavily search for questions beyond its catalog. It separately describes planned inference using a fine-tuned open-source model on Nebius H100s, with the prototype validated and production fine-tuning scheduled after launch.
Injester public prototype repositoryListed 21 Oct 2026 · GitHub · Web pageInjester’s prototype README identifies Tavily Extract API and Nebius Token Factory. Its architecture describes Nebius models restructuring extracted content, iterative prompt optimization and comparative agent benchmarks.
LaClawClaw provider implementation and deployment plans
Listed 21 Oct 2026 · Injester · Web page
LaClawClaw describes Tavily search for shopping questions beyond its catalog. It separately describes planned custom model inference on Nebius H100s, with a validated prototype and production fine-tuning scheduled after launch.
LaClawClaw provider implementation and deployment plansListed 21 Oct 2026 · Injester · Web pageLaClawClaw describes Tavily search for shopping questions beyond its catalog. It separately describes planned custom model inference on Nebius H100s, with a validated prototype and production fine-tuning scheduled after launch.
Founder account of Injester’s prototype implementation
Listed 21 Oct 2026 · LinkedIn · Web page
Benjamin Shyong describes Injester using Tavily Extract and Map and Nebius inference. He reports switching from Llama 70B to Nemotron 3 Super on Nebius and reducing workflow runtime to 90 seconds after further optimization.
Founder account of Injester’s prototype implementationListed 21 Oct 2026 · LinkedIn · Web pageBenjamin Shyong describes Injester using Tavily Extract and Map and Nebius inference. He reports switching from Llama 70B to Nemotron 3 Super on Nebius and reducing workflow runtime to 90 seconds after further optimization.
Injester hackathon submission
Listed 21 Oct 2026 · Cerebral Valley · Web page
Injester’s hackathon submission describes using Tavily to reduce website token noise and Nebius to restructure content for LLMs.
Injester hackathon submissionListed 21 Oct 2026 · Cerebral Valley · Web pageInjester’s hackathon submission describes using Tavily to reduce website token noise and Nebius to restructure content for LLMs.
Tavily newsletter describing Injester
Published 18 Mar 2026 · LinkedIn · Web page
Tavily identifies Injester as a hackathon project using its crawl and map endpoints and describes website noise reduction and iterative restructuring for agents.
Tavily newsletter describing InjesterPublished 18 Mar 2026 · LinkedIn · Web pageTavily identifies Injester as a hackathon project using its crawl and map endpoints and describes website noise reduction and iterative restructuring for agents.
Injester develops tools that help online merchants understand how AI shopping agents interpret their stores and identify missing information that can prevent products from being recommended. Initially focused on Shopify merchants, its workflow tests shopping scenarios, presents findings and supporting evidence, proposes improvements, and retests after changes. It examines details such as product fit, variants, availability, delivery and store policies. Merchants can arrange a founder-led demonstration and discuss an early engagement.
Injester helps merchants understand and improve how AI shopping agents interpret their stores. · Consumer
How they use Nebius and Tavily
Injester’s hackathon prototype used Tavily to extract website content and Nebius models to restructure it for AI agents. Its repository identifies Tavily Extract API and Nebius Token Factory, with an iterative prompt optimization loop and benchmarks comparing raw and restructured pages. Benjamin Shyong reports that switching from Llama 70B to Nemotron 3 Super and further optimization reduced the full workflow from nearly five minutes to 90 seconds.
Injester also powers LaClawClaw, an agent-oriented Shopify store. That product’s technical page describes Tavily search for questions beyond its catalog. It separately describes planned inference using a fine-tuned open-source model on Nebius H100s, with the prototype validated and production fine-tuning scheduled after launch.
Injester public prototype repositoryListed 21 Oct 2026 · GitHub · Web pageInjester’s prototype README identifies Tavily Extract API and Nebius Token Factory. Its architecture describes Nebius models restructuring extracted content, iterative prompt optimization and comparative agent benchmarks.
LaClawClaw provider implementation and deployment plans
Listed 21 Oct 2026 · Injester · Web page
LaClawClaw describes Tavily search for shopping questions beyond its catalog. It separately describes planned custom model inference on Nebius H100s, with a validated prototype and production fine-tuning scheduled after launch.
LaClawClaw provider implementation and deployment plansListed 21 Oct 2026 · Injester · Web pageLaClawClaw describes Tavily search for shopping questions beyond its catalog. It separately describes planned custom model inference on Nebius H100s, with a validated prototype and production fine-tuning scheduled after launch.
Founder account of Injester’s prototype implementation
Listed 21 Oct 2026 · LinkedIn · Web page
Benjamin Shyong describes Injester using Tavily Extract and Map and Nebius inference. He reports switching from Llama 70B to Nemotron 3 Super on Nebius and reducing workflow runtime to 90 seconds after further optimization.
Founder account of Injester’s prototype implementationListed 21 Oct 2026 · LinkedIn · Web pageBenjamin Shyong describes Injester using Tavily Extract and Map and Nebius inference. He reports switching from Llama 70B to Nemotron 3 Super on Nebius and reducing workflow runtime to 90 seconds after further optimization.
Injester hackathon submission
Listed 21 Oct 2026 · Cerebral Valley · Web page
Injester’s hackathon submission describes using Tavily to reduce website token noise and Nebius to restructure content for LLMs.
Injester hackathon submissionListed 21 Oct 2026 · Cerebral Valley · Web pageInjester’s hackathon submission describes using Tavily to reduce website token noise and Nebius to restructure content for LLMs.
Tavily newsletter describing Injester
Published 18 Mar 2026 · LinkedIn · Web page
Tavily identifies Injester as a hackathon project using its crawl and map endpoints and describes website noise reduction and iterative restructuring for agents.
Tavily newsletter describing InjesterPublished 18 Mar 2026 · LinkedIn · Web pageTavily identifies Injester as a hackathon project using its crawl and map endpoints and describes website noise reduction and iterative restructuring for agents.
Injester develops tools that help online merchants understand how AI shopping agents interpret their stores and identify missing information that can prevent products from being recommended. Initially focused on Shopify merchants, its workflow tests shopping scenarios, presents findings and supporting evidence, proposes improvements, and retests after changes. It examines details such as product fit, variants, availability, delivery and store policies. Merchants can arrange a founder-led demonstration and discuss an early engagement.
