Reka Edge

Analyze this product photo and suggest three specific improvements to lighting and composition that would increase conversion rates for an e-commerce listing.

Looking at your product image, I can identify three key improvements: First, the main light source creates a harsh shadow on the right side — diffusing it with a softbox would reveal more product detail. Second, the composition places the product slightly off-center left, which draws the eye away from the call-to-action zone; centering it would improve focus. Third, the background gradient competes with the product's own color palette — switching to a neutral white or light gray would make the product pop and reduce visual noise. These changes would likely improve click-through by making the product easier to evaluate at a glance.

Demonstrates multimodal analysis combining visual understanding with commercial reasoning. The 16K context window allows detailed image analysis alongside product guidelines. At $0.10/Mtok, this is cost-effective for batch processing product catalogs, though responses may lack the nuanced aesthetic judgment of larger vision models.

I'm debugging a React component that re-renders too often. Walk me through adding performance profiling without changing the component's behavior.

Start by wrapping your component export with `React.memo()` to establish a baseline — this won't fix the issue but will make re-renders visible. Next, add `console.log('ComponentName rendered')` at the top of your component function. In your parent component, wrap any callback props in `useCallback()` with appropriate dependencies, and wrap any computed values in `useMemo()`. Now run your app and watch the console: if you see repeated logs on actions that shouldn't trigger re-renders, check whether parent state changes are forcing updates. Use React DevTools Profiler to record a session and identify which props are changing unexpectedly.

Shows practical debugging guidance with step-by-step structure. Edge handles common development tasks efficiently at its price point. The relatively small context window means it works best with focused questions rather than large codebases, and may miss optimization patterns that require analyzing multiple file dependencies simultaneously.

Watch this 30-second video clip of a manufacturing line and identify any safety hazards or process inefficiencies you observe.

I've identified four concerns in this footage: At the 8-second mark, an operator reaches across the conveyor belt rather than walking around — this creates a crush hazard if the belt advances unexpectedly. Around 14 seconds, there's a gap in the product flow suggesting upstream batching issues that reduce throughput. At 22 seconds, the quality check station has products stacking up, indicating a bottleneck — consider adding a second inspector or simplifying the check process. Finally, throughout the clip, operators aren't wearing high-visibility vests in an area with forklift traffic, which violates most industrial safety standards.

Highlights video understanding capability, useful for safety audits or process documentation where visual context matters. The model can process video within its 16K token budget, making it practical for short clips. However, temporal reasoning across longer videos may be limited, and highly technical domain knowledge (specific machinery types, compliance codes) may require verification.

When Edge handles multimodal support tickets at $0.10/Mtok

A 4-person SaaS team gets 200 support tickets daily—half include screenshots, a quarter include screen recordings. Reka Edge processes all three modalities (text, image, video) at $0.10/Mtok in and out, making it the cheapest multimodal option for high-volume triage. The 16K context window covers most ticket threads plus attached media metadata. You're routing tickets to human agents based on sentiment and technical complexity, not generating long responses, so Edge's output quality threshold works. If you need to draft full replies or handle 50K-token video transcripts, you'll hit the context ceiling and want a larger model. For pure classification and routing at this price, Edge closes the case.

Edge wins on bulk image-to-metadata jobs under tight margins

A 10-person marketplace startup uploads 5,000 product photos weekly and needs structured tags (category, color, material, style) written to their catalog API. Reka Edge reads images and returns JSON at $0.10/Mtok—roughly $0.50 per 1,000 products when prompts and outputs stay compact. The 16K window is enough for a batch of 20-30 images with schema instructions in a single call. You're not asking for creative descriptions or handling edge cases that need reasoning; you're running a deterministic tagging pipeline. If accuracy dips below 92% on your validation set, budget for a second-pass model or human QA. For teams where cost per unit matters more than perfect precision, Edge delivers the margin.

When Edge pre-filters video uploads before human review

A 12-person community platform receives 800 user-submitted videos daily and needs to flag potential policy violations before they go live. Reka Edge ingests video natively and returns a risk score plus timestamp markers at $0.10/Mtok, letting you auto-approve low-risk content and route flagged clips to human moderators. The 16K context handles 90-second videos with prompt overhead; longer content requires chunking. You're not making final moderation decisions—Edge is the first-pass filter that cuts human review load by 60%. If false negatives cost you more than false positives, pair Edge with a second model or tighten the threshold. For platforms where speed and cost determine moderation scale, Edge opens the gate.