https://knowledged.to/notes/Recent content in Notes on knowledged.toHugoen-usWed, 27 May 2026 15:52:04 +0000https://knowledged.to/notes/humanities/anthropology/Wed, 27 May 2026 15:51:30 +0000https://knowledged.to/notes/humanities/anthropology/Holistic overview of anthropology, its branches, methods, distinctive perspective, topics, and applied uses.https://knowledged.to/notes/ml/commit-intent/Mon, 25 May 2026 17:31:24 +0000https://knowledged.to/notes/ml/commit-intent/Pattern where agents declare structured pre-tool intent for gating, auditability, and drift detection.https://knowledged.to/notes/ml/sub-agent-vs-tool-agent/Mon, 25 May 2026 17:16:33 +0000https://knowledged.to/notes/ml/sub-agent-vs-tool-agent/Distinguishes sub-agents from tool-agents by autonomy, interface, context, and harness contract.https://knowledged.to/notes/ml/llm-thinking-token-budgets/Mon, 25 May 2026 17:09:36 +0000https://knowledged.to/notes/ml/llm-thinking-token-budgets/Explains thinking-token budget parameters, provider naming, cost-latency tradeoffs, and completion-cap interactions.https://knowledged.to/notes/ml/llm-prompt-cache-provider-options/Thu, 21 May 2026 16:49:20 +0000https://knowledged.to/notes/ml/llm-prompt-cache-provider-options/Compares prompt/KV cache TTLs, controls, pricing, scope, and strategies across major LLM providers.https://knowledged.to/notes/ml/llm-prompt-caching-implicit-vs-explicit/Thu, 21 May 2026 16:08:55 +0000https://knowledged.to/notes/ml/llm-prompt-caching-implicit-vs-explicit/Explains implicit vs explicit LLM prompt caching, prefix constraints, provider support, and when to use each.https://knowledged.to/notes/ml/vectors-vs-tensors/Thu, 21 May 2026 15:49:59 +0000https://knowledged.to/notes/ml/vectors-vs-tensors/Explains how vectors relate to tensors in ML, including rank, framework terminology, and KV cache shapes.https://knowledged.to/notes/ml/llm-caching-input-tokens/Thu, 21 May 2026 15:43:26 +0000https://knowledged.to/notes/ml/llm-caching-input-tokens/Explains why LLM prompt caching applies to reusable input-token prefill, not sequential output decoding.https://knowledged.to/notes/ml/model-drift/Thu, 21 May 2026 15:33:36 +0000https://knowledged.to/notes/ml/model-drift/Overview of model drift, detection, mitigation, and LLM-specific issues like knowledge staleness and provider drift.https://knowledged.to/notes/ml/ppo-proximal-policy-optimization/Tue, 19 May 2026 17:18:44 +0000https://knowledged.to/notes/ml/ppo-proximal-policy-optimization/Overview of PPO, the clipped policy-gradient RL algorithm used in RLHF for InstructGPT and original ChatGPT.https://knowledged.to/notes/ml/grpo-group-relative-policy-optimization/Tue, 19 May 2026 17:17:58 +0000https://knowledged.to/notes/ml/grpo-group-relative-policy-optimization/Critic-free RL algorithm that replaces PPO's value model with group-relative rewards for LLM fine-tuning.https://knowledged.to/notes/ml/tool-dc-strategic-anchor-grouping-example/Tue, 19 May 2026 06:12:48 +0000https://knowledged.to/notes/ml/tool-dc-strategic-anchor-grouping-example/Concrete web-search example showing how Tool-DC strategic anchor grouping reduces schema-confusion in tool calls.https://knowledged.to/notes/ml/agentflow/Tue, 19 May 2026 05:08:59 +0000https://knowledged.to/notes/ml/agentflow/Overview of AgentFlow, an agent architecture that trains a planner with Flow-GRPO for multi-turn tool use.https://knowledged.to/notes/ml/tool-dc-framework/Tue, 19 May 2026 03:53:48 +0000https://knowledged.to/notes/ml/tool-dc-framework/Overview of Tool-DC, a try-check-retry framework for robust long-context tool-calling with large tool registries.https://knowledged.to/notes/ml/top-k-in-rag-search/Mon, 18 May 2026 16:11:28 +0000https://knowledged.to/notes/ml/top-k-in-rag-search/Explains top-k retrieval in RAG, tradeoffs for choosing k, reranking patterns, and similarity thresholds.https://knowledged.to/notes/fashion/skirts-trousers-gender-history/Sun, 17 May 2026 12:50:09 +0000https://knowledged.to/notes/fashion/skirts-trousers-gender-history/History of how skirts and trousers became gendered through riding, modesty, class, and Western fashion norms.https://knowledged.to/notes/ml/attention/Sun, 17 May 2026 05:54:45 +0000https://knowledged.to/notes/ml/attention/Explanation of the attention mechanism in ML, covering Query/Key/Value, self-attention, multi-head, causal, cross-attention, and efficiency variants like FlashAttention and GQA.https://knowledged.to/notes/fashion/pediculus-humanus-origin-of-clothing/Sun, 17 May 2026 04:43:15 +0000https://knowledged.to/notes/fashion/pediculus-humanus-origin-of-clothing/Study using body louse (Pediculus humanus) molecular clock to date the origin of regular clothing to ~72,000 years ago.https://knowledged.to/notes/fashion/paleolithic-eyed-needles-and-dress/Sun, 17 May 2026 04:38:21 +0000https://knowledged.to/notes/fashion/paleolithic-eyed-needles-and-dress/Review of how eyed needles (~40 kya) mark the shift from tailored clothing to layered garments and socially symbolic dress in the Paleolithic.https://knowledged.to/notes/ml/mcp-interaction-model/Sat, 16 May 2026 17:21:30 +0000https://knowledged.to/notes/ml/mcp-interaction-model/Deep-dive into the Model Context Protocol interaction model covering components, OAuth 2.1 authorization, initialization, capability discovery, tool calling, sampling, and elicitation.https://knowledged.to/notes/ml/ai-agents-in-go/Sat, 16 May 2026 15:58:30 +0000https://knowledged.to/notes/ml/ai-agents-in-go/Guide to building AI agents in Go using agent SDKs, with a minimal runnable example covering LLM integration, tools, and multi-agent workflows.https://knowledged.to/notes/ml/art-evaluation-rubric/Thu, 14 May 2026 13:02:47 +0000https://knowledged.to/notes/ml/art-evaluation-rubric/A six-dimension rubric (Beauty, Color, Texture, Content Detail, Line, Style) for evaluating AI-generated artworks, derived from traditional painting analysis principles.https://knowledged.to/notes/psychology/gestalt-principles/Thu, 14 May 2026 12:52:49 +0000https://knowledged.to/notes/psychology/gestalt-principles/Overview of Gestalt psychology principles describing how the mind organizes visual information, covering proximity, similarity, closure, and applications in design and AI.https://knowledged.to/notes/vocabulary/rubric/Thu, 14 May 2026 12:50:15 +0000https://knowledged.to/notes/vocabulary/rubric/Definition and etymology of "rubric", from Latin red-ink manuscript headings to its modern meaning as a structured evaluation guide.https://knowledged.to/notes/ml/commitment-gate/Wed, 13 May 2026 16:01:45 +0000https://knowledged.to/notes/ml/commitment-gate/Verification checkpoint in agent harnesses that blocks irreversible actions until cross-skill, cross-scale, and evidence-sufficiency checks pass.https://knowledged.to/notes/security/defense-in-depth/Fri, 01 May 2026 16:17:32 +0000https://knowledged.to/notes/security/defense-in-depth/Overview of the defense-in-depth security strategy, its layered controls, core assumptions, and applications beyond IThttps://knowledged.to/notes/ml/open-weight-models/Sun, 26 Apr 2026 15:36:37 +0000https://knowledged.to/notes/ml/open-weight-models/Explanation of open-weight models, their differences from closed and open-source models, and why they matter for local AI deployment and customization.https://knowledged.to/notes/ml/cross-entropy-in-ai/Sat, 25 Apr 2026 16:47:31 +0000https://knowledged.to/notes/ml/cross-entropy-in-ai/Explanation of cross-entropy as a loss function in AI, including intuition, formal definition, examples, and relationship to entropy and KL divergencehttps://knowledged.to/notes/ml/fine-tuning-techniques/Sat, 25 Apr 2026 15:53:49 +0000https://knowledged.to/notes/ml/fine-tuning-techniques/Comprehensive guide to LLM fine-tuning methods including full, parameter-efficient, and preference-based approaches with modern recipes and tools like LoRA and DPOhttps://knowledged.to/notes/ml/unsloth-studio-dataset-formats/Thu, 23 Apr 2026 16:39:20 +0000https://knowledged.to/notes/ml/unsloth-studio-dataset-formats/Overview of dataset formats supported by Unsloth Studio for fine-tuning, including JSONL, Alpaca, ShareGPT, ChatML, and Reasoning formats with rules and best practices and dataset size guidelineshttps://knowledged.to/notes/ml/mixture-of-experts/Thu, 23 Apr 2026 16:04:47 +0000https://knowledged.to/notes/ml/mixture-of-experts/Overview of MoE architecture, routing, key components, variants, and trade-offs in machine learning modelshttps://knowledged.to/notes/ml/chain-of-thought/Thu, 23 Apr 2026 15:53:32 +0000https://knowledged.to/notes/ml/chain-of-thought/Prompting technique where an AI model is guided — or learns — to reason through a problem step by step before arriving at the final answer, rather than jumping straight to the conclusion.https://knowledged.to/notes/ml/multimodal-visual-chain-of-thought/Wed, 22 Apr 2026 15:34:56 +0000https://knowledged.to/notes/ml/multimodal-visual-chain-of-thought/Extension of chain-of-thought prompting to multimodal settings where models reason step-by-step over both visual and textual information.https://knowledged.to/notes/ml/agent-harness-engineering/Fri, 17 Apr 2026 17:47:03 +0000https://knowledged.to/notes/ml/agent-harness-engineering/Overview of agent harness engineering — the scaffolding, infrastructure, and tooling surrounding an AI agent, covering execution environments, tool orchestration, memory management, control flow, tracing, safety, and state persistencehttps://knowledged.to/notes/ml/diffusion-models/Fri, 17 Apr 2026 16:56:26 +0000https://knowledged.to/notes/ml/diffusion-models/Overview of diffusion models, how they reverse a gradual noising process to generate data, key variants like DDPM, DDIM, and Latent Diffusion Models, and how text-to-image conditioning workshttps://knowledged.to/notes/ml/ai-prompts/Thu, 16 Apr 2026 22:42:26 +0530https://knowledged.to/notes/ml/ai-prompts/Overview of the different types of AI prompts including system, user, few-shot, zero-shot, chain-of-thought, meta, and retrieval-augmented promptshttps://knowledged.to/notes/ml/elastic-looped-transformers/Thu, 16 Apr 2026 22:36:40 +0530https://knowledged.to/notes/ml/elastic-looped-transformers/Overview of Elastic Looped Transformers, an adaptive compute architecture that loops a shallow transformer block multiple times to dynamically allocate compute based on input complexityhttps://knowledged.to/notes/ml/tempo-framework/Thu, 16 Apr 2026 22:15:24 +0530https://knowledged.to/notes/ml/tempo-framework/Overview of Tempo, a query-aware temporal compression framework for long-video understanding in multimodal AI, using a small VLM to filter relevant frames before passing a condensed representation to a large modelhttps://knowledged.to/notes/ml/memory-augmented-architectures/Thu, 16 Apr 2026 22:04:57 +0530https://knowledged.to/notes/ml/memory-augmented-architectures/Overview of memory-augmented neural network architectures that add dynamic external memory to models, covering NTMs, RAG, Memorizing Transformers, Titans, and practical implications for building persistent AI agentshttps://knowledged.to/notes/ml/forward-pass-and-single-pass/Thu, 16 Apr 2026 21:19:49 +0530https://knowledged.to/notes/ml/forward-pass-and-single-pass/Explanation of forward pass and single pass in LLMs, how transformer computation flows from embedding to output logits, and how speculative decoding exploits transformer parallelism to reduce large-model forward passeshttps://knowledged.to/notes/ml/speculative-decoding/Thu, 16 Apr 2026 20:54:05 +0530https://knowledged.to/notes/ml/speculative-decoding/Explanation of speculative decoding, an inference optimization that uses a fast draft model to propose tokens verified in parallel by a large model, achieving 2–3x throughput gains with identical output qualityhttps://knowledged.to/notes/ml/llm-model-weights/Mon, 13 Apr 2026 19:17:58 +0530https://knowledged.to/notes/ml/llm-model-weights/Explanation of what model weights are in LLMs, how they encode learned behaviour, why parameter count matters, and how systems like Ollama load them into memoryhttps://knowledged.to/notes/ml/gguf-models/Fri, 10 Apr 2026 09:11:52 +0530https://knowledged.to/notes/ml/gguf-models/Overview of the GGUF binary format for storing and distributing LLMs locally, including quantization levels, key characteristics, and popular runtimes like llama.cpp and Ollamahttps://knowledged.to/notes/ml/prompt-bias-in-ai/Thu, 09 Apr 2026 20:58:42 +0530https://knowledged.to/notes/ml/prompt-bias-in-ai/Explanation of prompt bias, how prompt wording and framing skew AI outputs, common forms including leading questions and assumption bias, and practical advice for writing neutral promptshttps://knowledged.to/notes/ml/primacy-bias-in-llm-style-selection/Wed, 08 Apr 2026 21:43:53 +0530https://knowledged.to/notes/ml/primacy-bias-in-llm-style-selection/Explanation of primacy bias in LLM selector prompts, how alphabetical candidate ordering caused over-selection of certain styles in BHQ, and fixes using deterministic non-lexicographic shufflinghttps://knowledged.to/notes/devops/slack-mcp-ideas/Wed, 08 Apr 2026 15:12:20 +0530https://knowledged.to/notes/devops/slack-mcp-ideas/Ideas for using Slack MCP to monitor automation opportunities and identify duplicated efforts within an organisationhttps://knowledged.to/notes/ml/elo-scoring-for-ai-models/Tue, 07 Apr 2026 22:15:56 +0530https://knowledged.to/notes/ml/elo-scoring-for-ai-models/Explanation of how ELO scoring is applied to rank AI models via human preference votes, including the math, strengths, weaknesses, and real-world use in Chatbot Arenahttps://knowledged.to/notes/ml/knowledge-distillation/Mon, 06 Apr 2026 22:43:20 +0530https://knowledged.to/notes/ml/knowledge-distillation/Overview of knowledge distillation, how student models learn from teacher model outputs, and applications including edge deployment, speculative decoding, and LLM traininghttps://knowledged.to/notes/ml/training-free-grpo/Mon, 06 Apr 2026 22:33:04 +0530https://knowledged.to/notes/ml/training-free-grpo/Overview of Training-Free GRPO, a method that improves LLM agent performance by updating model context (experience library) instead of parameters, achieving RL-like gains at a fraction of the costhttps://knowledged.to/notes/ml/attention-mechanism/Mon, 06 Apr 2026 22:18:46 +0530https://knowledged.to/notes/ml/attention-mechanism/Explanation of the attention mechanism in AI, including self-attention, cross-attention, multi-head attention, and the mathematical formulation behind Transformershttps://knowledged.to/notes/ml/transformer-architecture/Mon, 06 Apr 2026 22:15:42 +0530https://knowledged.to/notes/ml/transformer-architecture/Overview of the Transformer architecture including self-attention, multi-head attention, positional encoding, encoder-decoder structure, and key variants like BERT, GPT, and T5https://knowledged.to/notes/ml/recurrent-neural-networks/Mon, 06 Apr 2026 22:12:16 +0530https://knowledged.to/notes/ml/recurrent-neural-networks/Overview of RNNs, their memory mechanism, common variants (LSTM, GRU), use cases, and how they compare to Transformershttps://knowledged.to/notes/ml/rlhf-and-dpo/Mon, 06 Apr 2026 22:03:50 +0530https://knowledged.to/notes/ml/rlhf-and-dpo/Comparison of RLHF and DPO alignment techniques, covering their pipelines, strengths, weaknesses, and where each is used in practicehttps://knowledged.to/notes/ml/instruction-tuning/Mon, 06 Apr 2026 21:54:49 +0530https://knowledged.to/notes/ml/instruction-tuning/Overview of instruction tuning, how it works, dataset construction, and variants like RLHF, RLAIF, and DPOhttps://knowledged.to/notes/ml/perplexity-in-language-models/Mon, 06 Apr 2026 21:46:03 +0530https://knowledged.to/notes/ml/perplexity-in-language-models/Explanation of perplexity as a language model evaluation metric, including the formula, intuition, caveats, and relationship to cross-entropy losshttps://knowledged.to/notes/ml/model-quantization/Mon, 06 Apr 2026 21:39:37 +0530https://knowledged.to/notes/ml/model-quantization/Overview of model quantization techniques, precision levels, and trade-offs for reducing neural network memory and improving inference speedhttps://knowledged.to/notes/devops/gcloud-quick-reference/Mon, 06 Apr 2026 19:06:46 +0530https://knowledged.to/notes/devops/gcloud-quick-reference/Quick reference for common gcloud commands including authentication, project setup, and GKE cluster configurationhttps://knowledged.to/notes/devops/kubernetes-port-forward/Mon, 06 Apr 2026 18:59:45 +0530https://knowledged.to/notes/devops/kubernetes-port-forward/Quick reference for kubectl port-forward command syntax