Recall.ai
Recall.ai provides an API to get recordings, transcripts and metadata from video conferencing platforms like Zoom, Google Meet, Microsoft Teams, and m
Recall.ai is recognized for its innovative approach to improving AI memory and interaction through persistent, long-term recall across sessions. Users appreciate its capacity to enhance personalization and context awareness in AI models, contributing to more seamless interactions. However, there is a lack of specific user feedback regarding pricing, making it difficult to assess sentiment in that area. Overall, Recall.ai has a solid reputation for advancing the capabilities of AI memory effectively, though quantitative user reviews and broad-based mentions are limited.
Inference
Train, deploy, observe, and evaluate LLMs from a single platform. Lower cost, faster latency, and dedicated support from Inference.net.
Users frequently praise "Inference" for its efficient processing capabilities, particularly highlighted in the development of new optimization techniques that accelerate long-context AI model processing. However, there are notable concerns about the high costs associated with compute resources, suggesting pricing can often be a barrier for smaller operations. Discussions around pricing structures reveal some confusion and variability over appropriate multipliers for cost to price translations. Overall, "Inference" enjoys a strong reputation for performance but faces challenges regarding cost-effectiveness for broader market adoption.
Recall.ai
-75% vs last weekInference
Stable week-over-weekRecall.ai
Inference
Recall.ai
Inference
Recall.ai
Pricing found: $38, $0.50/hr, $0.15/h, $0.15/h, $0.15/h
Inference
Pricing found: $0, $1, $25, $250
Recall.ai (6)
Inference (8)
Only in Recall.ai (4)
Only in Inference (10)
Shared (1)
Only in Recall.ai (14)
Only in Inference (19)
Recall.ai
Inference
Recall.ai
Inference
Recall.ai
Inference
No YouTube channel
Recall.ai
.avif)
.avif)
.avif)
Inference
Recall.ai
Inference
Recall.ai
Is Opus 4.7's attention degradation a training direction problem? Some observations from heavy use
After working with Opus 4.7 for over two weeks, I noticed a subtle but persistent change in long conversations: the model's fundamental capabilities are still there, but the output feels filtered through something. Details that should be remembered get dropped, consistency drifts. It feels more like
Inference
Reviving PapersWithCode (by Hugging Face) [P]
Hi, Niels here from the open-source team at Hugging Face. Like many others, I was a huge fan of paperswithcode. Sadly, that website is no longer maintained after its acquisition by Meta. Hence, I've been working on reviving it. I obviously use AI agents to parse papers at scale and automatically g
Shared (3)
Only in Inference (1)
