· 5 min read
The Quiet Shift to Smaller, Specialized Models in 2025
Frontier models aren't always the answer. For inbox and search work, routing to small fine-tuned models is quietly becoming the default architecture.
All#ai#ai-agents#ai-productivity#applied-ai#automation#calendar#context-switching#deep-work#documents#email#engineering#engineering-leadership#invisible-ai#knowledge-work#llm-evaluation#product-updates#productivity#prompt-engineering#search#security#spreadsheets#startups#storage#tamaton#unified-workspace#workflow#workflows
· 4 min read
Designing Memory for AI Agents: What Actually Works
Memory isn't one feature. A practical breakdown of episodic, semantic, and working memory for AI agents — and how to wire them into real workflows.
· 4 min read
Why Your RAG Pipeline Retrieves the Wrong Chunks
A diagnostic framework for the quiet retrieval failures that degrade RAG quality — from chunking strategy to embedding mismatch.
· 4 min read
Why Your RAG Pipeline Retrieves the Wrong Chunks
Retrieval failures aren't one bug — they're three. A diagnostic framework for isolating chunking, embedding, and reranking problems instead of guessing.
· 5 min read
Context Windows Are Lying to You: The Lost-in-the-Middle Tax
Bigger context windows don't guarantee better recall. Here's where models actually lose information — and how to structure prompts so they don't.
· 4 min read
Search That Understands Your Files: Beyond Keyword Match
Why keyword search fails in modern workspaces, and how semantic search, metadata, and permission-aware retrieval combine to make file search actually usable.
· 4 min read
RAG vs Long Context: When to Retrieve, When to Stuff
Skip the 'long context killed RAG' debate. Here's a practical decision framework based on cost, latency, recall, and freshness.
· 4 min read
Beyond Chatbots: Pragmatic AI Workflows for Document Management
Embedded AI that categorizes files and drafts replies from your own data beats generic prompts. Here's how to build document workflows that actually save time.
· 4 min read
Why Your RAG Pipeline Retrieves the Wrong Chunks
Most RAG failures aren't the LLM's fault. Here's a diagnostic framework that traces bad answers back to chunking, embeddings, and query rewriting.
· 4 min read
Memory Systems for Long-Running AI Productivity Agents
Architectural patterns that give agents persistent memory across emails, documents, and projects — without exponential token costs.
· 4 min read
The Latency Tax: Why Unified Data Layers Beat App-Switching
Context switching isn't just a human problem. For AI agents, it's a measurable performance tax that only a unified data layer can eliminate.
· 3 min read
Unified Search Architecture: How Tamaton Queries Across Silos
A technical deep-dive into Tamaton's vector search implementation that seamlessly queries across email, documents, and files using embedded representations.
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