fastbuilderai/FastMemory
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prabhatkr
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new activity about 13 hours ago
snap-stanford/stark:100% SOTA achieved with Topological Memory updated a model about 13 hours ago
fastbuilderai/FastMemory updated a dataset about 13 hours ago
fastbuilderai/fastmemory-supremacy-benchmarksOrganizations
100% SOTA achieved with Topological Memory
#2 opened about 13 hours ago
by prabhatkr
updated a
model about 13 hours ago
updated a
dataset about 13 hours ago
posted an update about 15 hours ago
Post
57
Why did FastMemory achieve 100% accuracy at 10 million tokens while the SOTA is stalling at 64%?
It’s not because we have a better "retriever." It’s because we changed the Topology of Truth.
Standard RAG treats your data like a pile of leaves. You search for a leaf, and you hope you find the right one. As the pile grows to 10 million leaves, you inevitably fail.
FastMemory treats your data like a Building.
Topological Isolation: We use Rust-driven Louvain clustering to atomize text into "Logic Rooms."
Crystalline Grounding: Facts don't "decay" in our context; they are locked into a deterministic graph.
Sub-Second O(1) Routing: We don't "search" the haystack; we navigate the floor plan.
If your AI is mission-critical, "mostly right" is a liability. It’s time to move beyond the RAG pile and into the topological foundation.
Verify Yourself: fastbuilderai/fastmemory-supremacy-benchmarks
#MachineLearning #GraphAI #LouvainClustering #FastBuilder #EngineeringExcellence
It’s not because we have a better "retriever." It’s because we changed the Topology of Truth.
Standard RAG treats your data like a pile of leaves. You search for a leaf, and you hope you find the right one. As the pile grows to 10 million leaves, you inevitably fail.
FastMemory treats your data like a Building.
Topological Isolation: We use Rust-driven Louvain clustering to atomize text into "Logic Rooms."
Crystalline Grounding: Facts don't "decay" in our context; they are locked into a deterministic graph.
Sub-Second O(1) Routing: We don't "search" the haystack; we navigate the floor plan.
If your AI is mission-critical, "mostly right" is a liability. It’s time to move beyond the RAG pile and into the topological foundation.
Verify Yourself: fastbuilderai/fastmemory-supremacy-benchmarks
#MachineLearning #GraphAI #LouvainClustering #FastBuilder #EngineeringExcellence
posted an update 5 days ago
Post
115
AI fails to scale because it wasn't trained on the data it is expected to work.
Enterprise data is messier than the internet and web data that has been used to train the general models. That's why the bootup response of AI seems delightful. But that glory fades when rubber meets the road.
The other approaches like RAG, KG and Ontologies proved unworthy too.
In a recent hot out-of-the-press research, Topology beats RAG/KG/Ontology by a huge margin.
Download PDF: https://arxiv.org/html/2603.12458v1
Blog: https://fastbuilder.ai/blog/why-ai-fails-to-scale-why-topology-is-the-top-choice-for-the-enterprise
FastMemory is a topology builder for direct AI integration. Just 3 lines of code to build topology and wrap it in your LLM queries.
❌ You don't have to build embedding pipelines. Also no $$$ spent of embedding token usage.
❌ You also don't have to invest in heavy vector storage which is bigger in size than the underlying data. Vectors are 20%-50% bigger in size than text data storage.
✅ You only need the python app running the 'fastmemory' library and store the topology as graph in Neo4J or graphDB or any such storage.
✅ The stored topology is 10X-30X smaller than the data.
Get the magic of AI right away similar to native AI performance without heavy infra.
#topology #AI #RAG #Ontology #knowledgegraph #fastmemory
Enterprise data is messier than the internet and web data that has been used to train the general models. That's why the bootup response of AI seems delightful. But that glory fades when rubber meets the road.
The other approaches like RAG, KG and Ontologies proved unworthy too.
In a recent hot out-of-the-press research, Topology beats RAG/KG/Ontology by a huge margin.
Download PDF: https://arxiv.org/html/2603.12458v1
Blog: https://fastbuilder.ai/blog/why-ai-fails-to-scale-why-topology-is-the-top-choice-for-the-enterprise
FastMemory is a topology builder for direct AI integration. Just 3 lines of code to build topology and wrap it in your LLM queries.
❌ You don't have to build embedding pipelines. Also no $$$ spent of embedding token usage.
❌ You also don't have to invest in heavy vector storage which is bigger in size than the underlying data. Vectors are 20%-50% bigger in size than text data storage.
✅ You only need the python app running the 'fastmemory' library and store the topology as graph in Neo4J or graphDB or any such storage.
✅ The stored topology is 10X-30X smaller than the data.
Get the magic of AI right away similar to native AI performance without heavy infra.
#topology #AI #RAG #Ontology #knowledgegraph #fastmemory
Incredible Work (No issue, only props)
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3
#1 opened 7 days ago
by 
anongecko
posted an update 7 days ago
Post
173
A new SOTA achieved by FaastMemory - Cybersecurity can be truly agentic.
fastbuilderai/FastMemory
13. Adversarial Red-Team (Intel) 0.0% (Prompt Injection Hack) 14.8% (Database Leak) 🏆 100% (Zero-Hallucination Firewall)
fastbuilderai/FastMemory
13. Adversarial Red-Team (Intel) 0.0% (Prompt Injection Hack) 14.8% (Database Leak) 🏆 100% (Zero-Hallucination Firewall)
updated a
model 8 days ago
published a
model 8 days ago
Fastmemory achieved 100% on this benchmark
#3 opened 8 days ago
by prabhatkr
liked a
model 8 days ago
fastbuilderai/FastMemory
#8913 opened 8 days ago
by prabhatkr
published a
model 8 days ago
posted an update 9 days ago
Post
1276
🚀 Is Vector RAG Dead? Why We Built FastMemory to Beat PageIndex
If you've built a RAG pipeline for complex financial documents, you already know the painful truth: Standard vector search fails when things get complicated.
While tools like PageIndex and Mafin 2.5 provide great out-of-the-box PDF chat experiences, they hit structural bottlenecks the second you push them past basic queries.
We just published a comprehensive benchmark study comparing FastMemory against PageIndex across 5 advanced datasets. The results fundamentally change how we should think about document ingestion.
Read more: https://x.com/FastBuilderAI/status/2037404008978018493
If you've built a RAG pipeline for complex financial documents, you already know the painful truth: Standard vector search fails when things get complicated.
While tools like PageIndex and Mafin 2.5 provide great out-of-the-box PDF chat experiences, they hit structural bottlenecks the second you push them past basic queries.
We just published a comprehensive benchmark study comparing FastMemory against PageIndex across 5 advanced datasets. The results fundamentally change how we should think about document ingestion.
Read more: https://x.com/FastBuilderAI/status/2037404008978018493
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