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In Simple Terms with Satish

Podkast av Satish Choudhary

engelsk

Teknologi og vitenskap

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Les mer In Simple Terms with Satish

In Simple Terms with Satish breaks down complex ideas in technology into clear, calm, and easy-to-understand conversations. From artificial intelligence and blockchain to how computers work and future tech trends, each short episode uses simple stories and real-life examples to explain what’s really going on — without jargon, hype, or noise. Designed for curious learners who want clarity, not complexity. Technology explained without the noise.

Alle episoder

71 Episoder

episode Vector Database Index Design cover

Vector Database Index Design

Vector database index design is the way a retrieval system organizes stored vectors so it can find similar results fast enough, cheaply enough, and accurately enough for the real workload. In this episode, Satish uses a simple real-life example first, then turns the idea into a practical technical mental model for engineers and curious builders. In Simple Terms with Satish: daily tech trends explained simply, with enough technical depth for builders. Production note: This episode uses authorized synthetic narration based on Satish's own voice. The topic, script, and final editorial approval are by Satish. Engineer notes: Exact technical references: - pgvector supports exact and approximate nearest-neighbor search in Postgres. - pgvector says IVFFlat divides vectors into lists and searches a subset of nearby lists. - pgvector says IVFFlat builds faster and uses less memory than HNSW, but has lower query performance in the speed-recall tradeoff. - Pinecone documents one index as a place that can combine dense vectors, sparse vectors, full-text search, and metadata filtering. - Pinecone says one index per use case is the typical pattern. - Milvus documents FLAT, IVF_FLAT, IVF_SQ8, IVF_PQ, HNSW, DISKANN, and sparse inverted indexes. - Milvus recommends indexing both vector fields and scalar fields that are frequently accessed. Sources: - https://github.com/pgvector/pgvector - https://docs.pinecone.io/guides/index-data/indexing-overview - https://milvus.io/docs/index-vector-fields.md

I går - 3 min
episode WebAssembly Sandboxing For AI Tools cover

WebAssembly Sandboxing For AI Tools

WebAssembly sandboxing is a way to run code inside a controlled environment where the code only gets the specific access that the host system decides to give it. In this episode, Satish uses a simple real-life example first, then turns the idea into a practical technical mental model for engineers and curious builders. In Simple Terms with Satish: daily tech trends explained simply, with enough technical depth for builders. Production note: This episode uses authorized synthetic narration based on Satish's own voice. The topic, script, and final editorial approval are by Satish. Engineer notes: Exact technical references: - Wasmtime says one of WebAssembly's main goals is to execute untrusted code safely inside a sandbox. - Wasmtime says outside-world interaction happens only through explicit imports and exports. - Wasmtime documents WASI filesystem access as capability-based. - WASI.dev describes WASI as a secure standard interface for Wasm software across many environments. - The Component Model docs describe components as interoperable building blocks and note that WASI 0.2.0 is the current stable release. - Cloudflare Workers describes sandboxing as secure isolation plus API design, with isolates and stricter process-level controls where needed. Sources: - https://docs.wasmtime.dev/security.html - https://wasi.dev/ - https://component-model.bytecodealliance.org/ - https://developers.cloudflare.com/workers/reference/security-model/

12. juni 2026 - 3 min
episode Workflow State Machines cover

Workflow State Machines

A workflow state machine is the part of a system that keeps track of where a multi-step process is, what already happened, what should happen next, and what to do if something fails. In this episode, Satish uses a simple real-life example first, then turns the idea into a practical technical mental model for engineers and curious builders. In Simple Terms with Satish: daily tech trends explained simply, with enough technical depth for builders. Production note: This episode uses authorized synthetic narration based on Satish's own voice. The topic, script, and final editorial approval are by Satish. Engineer notes: Exact technical references: - OpenAI documents background mode for long-running asynchronous tasks. - The OpenAI Agents SDK exposes resumable run state after interruptions through `to_state()`. - Temporal says workflows can continue after crashes by replaying event history and can keep running for years. - Temporal retries activities declaratively instead of requiring custom retry code everywhere. - Azure Durable Task uses event sourcing and append-only orchestration history to rebuild state on replay. - AWS Step Functions exposes retry controls like interval, backoff, max delay, and jitter inside workflow definitions. Sources: - https://developers.openai.com/api/docs/guides/background - https://openai.github.io/openai-agents-python/results/ - https://docs.temporal.io/workflows - https://docs.temporal.io/encyclopedia/retry-policies - https://learn.microsoft.com/en-us/azure/durable-task/common/durable-task-orchestrations - https://docs.aws.amazon.com/step-functions/latest/dg/concepts-error-handling.html

11. juni 2026 - 3 min
episode Semantic Reranking cover

Semantic Reranking

Semantic reranking is the step where an AI system takes a shortlist of search results and reorders them based on which result best matches the meaning of your question. In this episode, Satish uses a simple real-life example first, then turns the idea into a practical technical mental model for engineers and curious builders. In Simple Terms with Satish: daily tech trends explained simply, with enough technical depth for builders. Production note: This episode uses authorized synthetic narration based on Satish's own voice. The topic, script, and final editorial approval are by Satish. Engineer notes: Exact technical references: - Azure AI Search semantic ranker reranks an initial BM25-ranked or RRF-ranked result set and is built into agentic retrieval. - Azure semantic ranking applies to a bounded result window rather than searching the full corpus again. - Pinecone exposes reranking as part of a two-stage retrieval process and also as a standalone operation. - Cohere documents reranking for semi-structured and tabular data in addition to plain text. - OpenSearch documents rerank as a search response processor using a cross-encoder model. Sources: - https://learn.microsoft.com/en-us/azure/search/semantic-search-overview - https://docs.pinecone.io/guides/search/rerank-results - https://docs.cohere.com/docs/reranking-with-cohere - https://docs.opensearch.org/latest/search-plugins/search-pipelines/search-processors/

10. juni 2026 - 2 min
episode Hybrid RAG Pipelines cover

Hybrid RAG Pipelines

That is the pattern where an AI system looks up outside information before it answers. In this episode, Satish uses a simple real-life example first, then turns the idea into a practical technical mental model for engineers and curious builders. In Simple Terms with Satish: daily tech trends explained simply, with enough technical depth for builders. Production note: This episode uses authorized synthetic narration based on Satish's own voice. The topic, script, and final editorial approval are by Satish. Engineer notes: Exact technical references: - OpenAI file search uses both semantic and keyword search. - Azure AI Search hybrid search runs full-text and vector queries in parallel and merges them with Reciprocal Rank Fusion. - Azure query guidance recommends starting with balanced hybrid patterns instead of maxing out every retrieval parameter. - Pinecone documents reranking as a second-stage quality improvement after merged retrieval. - Cohere documents reranking as a semantic boost for keyword or vector retrieval systems. Sources: - https://platform.openai.com/docs/guides/tools-file-search/ - https://learn.microsoft.com/en-us/azure/search/hybrid-search-overview - https://learn.microsoft.com/en-us/azure/search/hybrid-search-how-to-query - https://docs.pinecone.io/guides/search/hybrid-search - https://docs.pinecone.io/guides/search/rerank-results - https://docs.cohere.com/docs/reranking-quickstart

9. juni 2026 - 3 min
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