https://learn.aathan.in Clear, technical write-ups on algorithms, AI agents, design, UX, psychology, and building things — by Aathan A. en Sun, 06 Sep 2026 04:04:34 GMT https://learn.aathan.in/docs/search-issues https://learn.aathan.in/docs/search-issues Sun, 06 Sep 2026 09:30:00 GMT Combinatorial explosion, cycles and redundant paths, local maxima, plateaux and ridges, inadmissible and inconsistent heuristics, and the four classic design questions — with a diagnostic table for telling which failure you are actually looking at. artificial-intelligencesearchcombinatorial-explosionlocal-maximaplateauridgehill-climbingheuristicsadmissibilitydebugging https://learn.aathan.in/docs/state-space-search https://learn.aathan.in/docs/state-space-search Sun, 06 Sep 2026 09:00:00 GMT The formal five-part problem definition, why a small state space produces an infinite search tree, the difference between a node and a state, tree-search vs graph-search, and the completeness and complexity of every uninformed strategy. artificial-intelligencestate-spacesearch-treegraph-searchbfsdfsiterative-deepeningcomplexityproblem-formulation https://learn.aathan.in/docs/vacuum-cleaner-problem https://learn.aathan.in/docs/vacuum-cleaner-problem Sat, 05 Sep 2026 10:30:00 GMT The smallest AI problem there is — eight states, three actions. Used to define what an agent is, what makes one rational, and what happens when it can't see the whole world. artificial-intelligencevacuum-worldagentsrationalitypeaspartial-observabilityreflex-agent https://learn.aathan.in/docs/monkey-and-banana-problem https://learn.aathan.in/docs/monkey-and-banana-problem Sat, 05 Sep 2026 10:20:00 GMT A monkey, a box, and a banana hanging out of reach. The four-part state tuple, the operators, why this problem became the standard test for logical planning, and a full solver in Python and Prolog. artificial-intelligencemonkey-and-bananaplanningmeans-ends-analysisprologstate-space https://learn.aathan.in/docs/water-jug-problem https://learn.aathan.in/docs/water-jug-problem Sat, 05 Sep 2026 10:10:00 GMT Two unmarked jugs, no measuring marks, and a target volume. The six production rules, the complete 20-state graph drawn out, the number-theory result that says when it's solvable, and BFS code. artificial-intelligencewater-jugproduction-rulesbfsstate-spacegcdbezout https://learn.aathan.in/docs/n-queens-problem https://learn.aathan.in/docs/n-queens-problem Sat, 05 Sep 2026 10:00:00 GMT Place N queens on an N×N board so none attack each other. The full 4-queens backtracking trace, why the naive space is 16 million and the smart one is 40,320, plus hill climbing and min-conflicts. artificial-intelligencen-queens8-queensbacktrackingconstraint-satisfactionhill-climbingmin-conflicts https://learn.aathan.in/docs/travelling-salesman-problem https://learn.aathan.in/docs/travelling-salesman-problem Sat, 05 Sep 2026 09:50:00 GMT Visit every city once, return home, minimise distance. Why brute force dies at 20 cities, how branch-and-bound and the MST heuristic help, and the approximation algorithms that actually ship. artificial-intelligencetravelling-salesmantspnp-hardbranch-and-boundheuristicsoptimisation https://learn.aathan.in/docs/missionaries-and-cannibals https://learn.aathan.in/docs/missionaries-and-cannibals Sat, 05 Sep 2026 09:40:00 GMT Three missionaries, three cannibals, one two-seat boat. The state representation that makes it tractable, why 12 of the 32 states are illegal, the full 11-move solution traced, and working BFS code. artificial-intelligencemissionaries-and-cannibalsriver-crossingbfsconstraintsstate-space https://learn.aathan.in/docs/block-world-problem https://learn.aathan.in/docs/block-world-problem Sat, 05 Sep 2026 09:30:00 GMT A robot arm stacking blocks — the problem that gave us predicate representation, the STRIPS planner, goal stacks, and the Sussman anomaly that broke naive planning for a decade. artificial-intelligenceblock-worldstripsplanningpredicate-logicsussman-anomaly https://learn.aathan.in/docs/tic-tac-toe https://learn.aathan.in/docs/tic-tac-toe Sat, 05 Sep 2026 09:20:00 GMT The classic two-player game as an AI problem — the game tree, why minimax guarantees perfect play, how alpha-beta pruning cuts the work by 90%, and a complete unbeatable player in Python. artificial-intelligencetic-tac-toeminimaxalpha-beta-pruninggame-treeadversarial-search https://learn.aathan.in/docs/8-puzzle https://learn.aathan.in/docs/8-puzzle Sat, 05 Sep 2026 09:10:00 GMT The canonical AI search problem. State representation, the four operators, why half of all boards are unsolvable, the misplaced-tiles and Manhattan-distance heuristics, and a full A* implementation with a traced solution. artificial-intelligence8-puzzlestate-spacea-starheuristicsmanhattan-distancebfs https://learn.aathan.in/docs/ai-problem-solving-overview https://learn.aathan.in/docs/ai-problem-solving-overview Sat, 05 Sep 2026 09:00:00 GMT Every classic AI problem — 8-puzzle, water jug, missionaries and cannibals, N-queens — is the same thing underneath: a state space you search. Here is the shared vocabulary, the search strategies, and how to pick one. artificial-intelligenceproblem-solvingstate-spacesearchbfsdfsa-starheuristics https://learn.aathan.in/docs/ml-model-evaluation https://learn.aathan.in/docs/ml-model-evaluation Thu, 20 Aug 2026 11:30:00 GMT MAE, MSE, RMSE and R² for regression; the confusion matrix, precision, recall, F1 and ROC-AUC for classification — what each number means, when accuracy lies, and how to diagnose overfitting. machine-learningmetricsevaluationconfusion-matrixprecision-recallrmseoverfitting https://learn.aathan.in/docs/ml-classification-algorithms https://learn.aathan.in/docs/ml-classification-algorithms Thu, 20 Aug 2026 11:00:00 GMT Every major classifier explained — logistic regression, KNN, naive Bayes, decision trees, random forests, SVM and gradient boosting — how each draws its decision boundary, when to use it, and the code. machine-learningclassificationlogistic-regressionknnsvmrandom-forestnaive-bayes https://learn.aathan.in/docs/ml-regression-algorithms https://learn.aathan.in/docs/ml-regression-algorithms Thu, 20 Aug 2026 10:30:00 GMT Polynomial regression, Ridge, Lasso and ElasticNet regularisation, decision tree and random forest regressors, gradient boosting and SVR — what each fixes, when to use it, and the code. machine-learningregressionridgelassorandom-forestxgboostregularization https://learn.aathan.in/docs/ml-linear-regression https://learn.aathan.in/docs/ml-linear-regression Thu, 20 Aug 2026 10:00:00 GMT The foundational regression algorithm — the best-fit line derived by hand, reproduced in scikit-learn, then a full real-dataset workflow with train/test split, standardisation and test-set metrics. machine-learninglinear-regressionregressionscikit-learnrmseleast-squares https://learn.aathan.in/docs/ml-data-preprocessing https://learn.aathan.in/docs/ml-data-preprocessing Thu, 20 Aug 2026 09:30:00 GMT Turning raw data into something a model accepts — splitting X and y, imputing missing values, one-hot and label encoding, the train/test split, and feature scaling — worked end to end on a real messy dataset. machine-learningpreprocessingscikit-learnencodingimputationtrain-test-split https://learn.aathan.in/docs/ml-supervised-overview https://learn.aathan.in/docs/ml-supervised-overview Thu, 20 Aug 2026 09:00:00 GMT The fundamental split in supervised learning — predicting a number versus predicting a category — with the full algorithm taxonomy, how to tell which problem you have, and the workflow every model follows. machine-learningsupervised-learningclassificationregressionscikit-learn https://learn.aathan.in/docs/statistics-skewness-kurtosis https://learn.aathan.in/docs/statistics-skewness-kurtosis Sun, 09 Aug 2026 09:30:00 GMT The third and fourth moments explained with graphs — every skewness type (right, left, symmetric) and kurtosis type (lepto, meso, platy), their formulas, how to read the numbers, and how to fix skewed data. inferential-statisticsstatisticsskewnesskurtosisdistributionnormal-distributionoutliers https://learn.aathan.in/docs/statistics-overview https://learn.aathan.in/docs/statistics-overview Sun, 09 Aug 2026 09:00:00 GMT Complete notes from the Inferential Statistics sessions — descriptive statistics, central tendency, dispersion, quartiles, IQR and box plots, with worked examples and industry applications. inferential-statisticsstatisticsstudy-notesindex https://learn.aathan.in/docs/statistics-variable-types https://learn.aathan.in/docs/statistics-variable-types Sun, 09 Aug 2026 09:00:00 GMT Numerical vs categorical variables in full — discrete, continuous, nominal, ordinal, unstructured data — plus the two classification traps that catch everyone. inferential-statisticsstatisticsvariablesdata-typescategoricalnumerical https://learn.aathan.in/docs/statistics-applications https://learn.aathan.in/docs/statistics-applications Sun, 09 Aug 2026 09:00:00 GMT Which measure to use when, outliers as anomaly detection vs noise, using standard deviation for feature selection, and where statistics sits in an AI/ML workflow. inferential-statisticsstatisticsanomaly-detectionfeature-selectionmachine-learning https://learn.aathan.in/docs/statistics-foundations https://learn.aathan.in/docs/statistics-foundations Sun, 09 Aug 2026 09:00:00 GMT The split between descriptive and inferential statistics, worked classification examples, and why it matters in industry — A/B testing, feature engineering and AI bias detection. inferential-statisticsstatisticsdescriptive-statisticsab-testingai-bias https://learn.aathan.in/docs/statistics-central-tendency https://learn.aathan.in/docs/statistics-central-tendency Sun, 09 Aug 2026 09:00:00 GMT Mean, median and mode — formulas, merits and demerits, which to use for missing values, what outliers destroy, and how skewness shifts the three apart. inferential-statisticsstatisticsmeanmedianmodeoutliersskewness https://learn.aathan.in/docs/statistics-dispersion https://learn.aathan.in/docs/statistics-dispersion Sun, 09 Aug 2026 09:00:00 GMT Why the average alone is never enough — range, variance, standard deviation, the population vs sample (n-1) question, Excel walkthroughs, and the coefficient of variation. inferential-statisticsstatisticsvariancestandard-deviationrangecoefficient-of-variation https://learn.aathan.in/docs/statistics-quartiles-boxplots https://learn.aathan.in/docs/statistics-quartiles-boxplots Sun, 09 Aug 2026 09:00:00 GMT Quartiles and the interquartile range, the 1.5 x IQR outlier rule, the five-number summary, and how to build and read a box plot — with fully worked examples. inferential-statisticsstatisticsquartilesiqrbox-plotfive-number-summaryoutliers https://learn.aathan.in/docs/statistics-reference https://learn.aathan.in/docs/statistics-reference Sun, 09 Aug 2026 09:00:00 GMT The complete formula sheet, every number used in class, the exam traps, student questions answered, and the ten things worth memorising. inferential-statisticsstatisticsformula-sheetrevisionexam-prep https://learn.aathan.in/docs/python-eda https://learn.aathan.in/docs/python-eda Mon, 20 Jul 2026 10:30:00 GMT What EDA is and why it comes before modelling — plotting with Pandas and Matplotlib (line, scatter, histogram), and the three levels of analysis: univariate, bivariate and multivariate. pythonedamatplotlibvisualizationdata-sciencepandas https://learn.aathan.in/docs/python-pandas https://learn.aathan.in/docs/python-pandas Mon, 20 Jul 2026 10:00:00 GMT A complete introduction to Pandas — Series and DataFrames, loc vs iloc, reading CSV and JSON, inspecting data with head/tail/info, and the full data-cleaning workflow for empty cells, wrong data and duplicates. pythonpandasdataframedata-cleaningdata-sciencecsv https://learn.aathan.in/docs/python-numpy https://learn.aathan.in/docs/python-numpy Mon, 20 Jul 2026 09:30:00 GMT Getting started with NumPy — why arrays beat lists (with real speed and memory measurements), creating ndarrays, dimensions, dtypes, indexing and slicing in 1-D and 2-D, and the array-generating helpers. pythonnumpyarraysndarraydata-scienceperformance https://learn.aathan.in/docs/python-sets https://learn.aathan.in/docs/python-sets Mon, 20 Jul 2026 09:00:00 GMT Everything about Python sets — creating them, why they're unordered and unique, add/update, the four ways to remove, all the set operations (union, intersection, difference), frozensets, and when to use a set instead of a list. pythonsetsdata-structuresbasicsdeduplication https://learn.aathan.in/docs/python-tuples https://learn.aathan.in/docs/python-tuples Sun, 19 Jul 2026 09:30:00 GMT Everything about Python tuples — creating (and the one-element comma trap), packing & unpacking, immutability, slicing, the two methods, named tuples, when to use a tuple vs a list — with a runnable example for each. pythontuplesdata-structuresunpackingbasics https://learn.aathan.in/docs/python-lists-dicts https://learn.aathan.in/docs/python-lists-dicts Sun, 19 Jul 2026 09:00:00 GMT Everything about Python's two workhorse data structures — creating, indexing, slicing, every method, comprehensions, nesting, copying, performance, and the gotchas — with a runnable example for every concept. pythonlistsdictsdata-structuresbasicscomprehensions https://learn.aathan.in/docs/doppler-effect https://learn.aathan.in/docs/doppler-effect Sat, 18 Jul 2026 10:00:00 GMT The shift in wave frequency from relative motion — the intuition, the formulas for sound and light (with signs that actually make sense), worked examples, and where it shows up from radar guns to redshift. physicswavesdopplerfrequencyrelativitysound https://learn.aathan.in/docs/logical-reasoning https://learn.aathan.in/docs/logical-reasoning Sat, 18 Jul 2026 09:30:00 GMT A complete tour of logical reasoning problems — syllogisms, series, analogies, coding-decoding, blood relations, directions, seating arrangements, knights & knaves, grid puzzles, and conditional logic — each solved step by step. mathlogicreasoningaptitudepuzzlesproblem-solving https://learn.aathan.in/docs/growth-mindset https://learn.aathan.in/docs/growth-mindset Sat, 18 Jul 2026 09:00:00 GMT Carol Dweck's mindset research explained clearly — what fixed and growth mindsets really are, how they play out in daily situations, what the evidence honestly shows, and how to actually shift yours. psychologygrowth-mindsetfixed-mindsetlearningdweckmotivation https://learn.aathan.in/docs/figma-to-code-examples https://learn.aathan.in/docs/figma-to-code-examples Fri, 17 Jul 2026 10:20:00 GMT Three complete Figma-to-Tailwind conversions — a responsive navbar, a pricing card, and a dashboard stats grid — each from inspector specs to final HTML, plus the AI-assisted workflow. figmatailwindhtmlcomponentsexamplesfrontend https://learn.aathan.in/docs/figma-to-tailwind https://learn.aathan.in/docs/figma-to-tailwind Fri, 17 Jul 2026 10:00:00 GMT How to turn a full Figma design into clean HTML + Tailwind CSS — reading Dev Mode, mapping tokens and Auto Layout to Tailwind, and a worked hero-section example with complete code. figmatailwindhtmlcssfrontenddesign-handoff https://learn.aathan.in/docs/rag-latency https://learn.aathan.in/docs/rag-latency Fri, 17 Jul 2026 09:20:00 GMT The latest techniques behind fast RAG responses — streaming and TTFT, ANN indexes, quantized embeddings, semantic and prompt caching, parallel retrieval, and speculative decoding. ai-agentsraglatencycachingstreamingperformance https://learn.aathan.in/docs/rag-advanced https://learn.aathan.in/docs/rag-advanced Fri, 17 Jul 2026 09:00:00 GMT The techniques that separate a demo RAG pipeline from a production one — hybrid search, reranking, query transforms, contextual retrieval, GraphRAG, and agentic RAG. ai-agentsraghybrid-searchrerankinggraphragretrieval https://learn.aathan.in/docs/ux-laws https://learn.aathan.in/docs/ux-laws Thu, 16 Jul 2026 17:20:00 GMT The named psychological principles behind good interfaces — Hick's, Fitts's, Miller's, Jakob's, Zeigarnik, Peak–End and more — each with how to apply it and example UIs. uxpsychologylaws-of-uxprinciplesusability https://learn.aathan.in/docs/user-behavior https://learn.aathan.in/docs/user-behavior Thu, 16 Jul 2026 17:10:00 GMT People don't read, they scan; they don't optimize, they satisfice; they ignore what looks like an ad and never open the manual. Design for the real user, not the ideal one. uxpsychologyuser-behaviorusabilitypatterns https://learn.aathan.in/docs/mental-models https://learn.aathan.in/docs/mental-models Thu, 16 Jul 2026 17:00:00 GMT Users arrive with a belief about how your interface works, formed by every app they've used before. Good design matches that belief instead of fighting it. uxpsychologymental-modelsdesignusability https://learn.aathan.in/docs/diy-automate-a-chore https://learn.aathan.in/docs/diy-automate-a-chore Thu, 16 Jul 2026 16:00:00 GMT Make the robots do your repetitive busywork — saving attachments, sending reminders, syncing forms — by connecting apps in Zapier or Make. No code, big payoff. diyno-codezapiermakeautomationproductivity https://learn.aathan.in/docs/diy-web-app-from-prompt https://learn.aathan.in/docs/diy-web-app-from-prompt Thu, 16 Jul 2026 15:50:00 GMT Describe an app — with logins and a database — in plain English, and Lovable builds and deploys the whole thing. The most ambitious no-code project, still no code. diyno-codelovablefull-stackapps https://learn.aathan.in/docs/diy-sheet-to-app https://learn.aathan.in/docs/diy-sheet-to-app Thu, 16 Jul 2026 15:40:00 GMT Put your data in a spreadsheet, connect it to Glide, and get a real installable phone app — habit tracker, collection catalog, expense log — with zero code. diyno-codeglidegoogle-sheetsapps https://learn.aathan.in/docs/diy-telegram-bot https://learn.aathan.in/docs/diy-telegram-bot Thu, 16 Jul 2026 15:30:00 GMT A bot that messages you a quote, reminder, or weather update every morning — created with Telegram's BotFather and a no-code Make automation. No server, no code. diyno-codetelegrammakeautomationbots https://learn.aathan.in/docs/diy-browser-game https://learn.aathan.in/docs/diy-browser-game Thu, 16 Jul 2026 15:20:00 GMT Describe a game in plain English, and Google AI Studio builds a real, playable web game you can deploy and send to friends. The same path used to build a game on games.aathan.in. diyno-codegoogle-ai-studiogamesgemini https://learn.aathan.in/docs/diy-personal-dashboard https://learn.aathan.in/docs/diy-personal-dashboard Thu, 16 Jul 2026 15:10:00 GMT A one-screen dashboard — clock, weather, to-dos, and a daily quote — built by just chatting with Claude. No code, about ten minutes. diyno-codeclaude-artifactsdashboardbeginners https://learn.aathan.in/docs/diy-overview https://learn.aathan.in/docs/diy-overview Thu, 16 Jul 2026 15:00:00 GMT You don't need to be a programmer to build real, working, genuinely useful things anymore. Here's the mindset, the toolbox, and a set of fun projects to build this weekend. diyno-codevibe-codingprojectsbeginners https://learn.aathan.in/docs/emotions-and-stress https://learn.aathan.in/docs/emotions-and-stress Thu, 16 Jul 2026 14:50:00 GMT What emotions actually are, how the stress response works, why the Yerkes–Dodson curve matters for performance, and the regulation strategies with real evidence — reappraisal, not suppression. psychologyemotionsstressemotion-regulationwellbeing https://learn.aathan.in/docs/social-psychology https://learn.aathan.in/docs/social-psychology Thu, 16 Jul 2026 14:40:00 GMT Conformity, obedience, persuasion, and the attribution errors we all make — the classic experiments, what modern re-examination changed, and the defence kit. psychologysocial-psychologypersuasionconformityinfluence https://learn.aathan.in/docs/motivation-and-habits https://learn.aathan.in/docs/motivation-and-habits Thu, 16 Jul 2026 14:30:00 GMT Intrinsic vs extrinsic motivation, what dopamine actually does, the cue–routine–reward loop, and the evidence-backed mechanics of building habits that survive contact with real life. psychologymotivationhabitsdopaminebehavior-change https://learn.aathan.in/docs/memory-and-learning https://learn.aathan.in/docs/memory-and-learning Thu, 16 Jul 2026 14:20:00 GMT The three-stage memory model, why working memory is the bottleneck, the forgetting curve, and the two techniques with the strongest evidence in all of psychology — spacing and retrieval practice. psychologymemorylearningspaced-repetitionretrieval-practice https://learn.aathan.in/docs/cognitive-biases https://learn.aathan.in/docs/cognitive-biases Thu, 16 Jul 2026 14:10:00 GMT System 1 vs System 2, the heuristics that usually serve us well, and the systematic errors they produce — anchoring, availability, loss aversion, confirmation bias, and how to actually debias. psychologycognitive-biasesheuristicskahnemandecision-making https://learn.aathan.in/docs/psychology-overview https://learn.aathan.in/docs/psychology-overview Thu, 16 Jul 2026 14:00:00 GMT What psychology actually is, the big schools of thought, the branches worth knowing, and how to read psychological claims without being fooled. psychologycognitive-sciencebehaviorismreplication-crisisbasics https://learn.aathan.in/docs/dashboard-examples https://learn.aathan.in/docs/dashboard-examples Thu, 16 Jul 2026 13:30:00 GMT What to steal from the best real-world dashboards — Stripe, Linear, Vercel, Datadog, Grafana, Plausible and more — plus the anti-patterns to avoid. designdashboardsui-uxcase-studiesinspiration https://learn.aathan.in/docs/dashboard-optimization https://learn.aathan.in/docs/dashboard-optimization Thu, 16 Jul 2026 13:20:00 GMT Practical UX and performance techniques that make a dashboard feel fast, calm, and effortless — from skeleton loaders to server-side aggregation. designdashboardsperformanceui-uxoptimization https://learn.aathan.in/docs/dashboard-design-principles https://learn.aathan.in/docs/dashboard-design-principles Thu, 16 Jul 2026 13:10:00 GMT The core rules of dashboard UX — glanceability, visual hierarchy, layout patterns, choosing the right chart, and using color and numbers with intent. designdashboardsui-uxdata-vizlayout https://learn.aathan.in/docs/dashboards-evolution https://learn.aathan.in/docs/dashboards-evolution Thu, 16 Jul 2026 13:00:00 GMT What a dashboard really is, the three types, how dashboard UX evolved from static reports to real-time products, and where it's heading next. designdashboardsui-uxdata-viztrends https://learn.aathan.in/docs/omniparser https://learn.aathan.in/docs/omniparser Thu, 16 Jul 2026 13:00:00 GMT Microsoft's OmniParser is the "eyes" of a GUI agent — a vision pipeline that converts a raw screenshot into a labelled map of clickable elements. Here's how it works and why it's fundamentally not an LLM. ai-agentsgui-agentscomputer-usevisiongroundingomniparser https://learn.aathan.in/docs/context-engineering https://learn.aathan.in/docs/context-engineering Thu, 16 Jul 2026 11:40:00 GMT The discipline of curating exactly what goes into an LLM's limited context window — the successor to prompt engineering. ai-agentscontextpromptingmemorytokens https://learn.aathan.in/docs/multi-agent-systems https://learn.aathan.in/docs/multi-agent-systems Thu, 16 Jul 2026 11:30:00 GMT When one agent isn't enough — how orchestrators, specialized subagents, and handoffs divide work among many agents. ai-agentsmulti-agentorchestrationsubagents https://learn.aathan.in/docs/agentic-loop https://learn.aathan.in/docs/agentic-loop Thu, 16 Jul 2026 11:20:00 GMT The reason–act–observe cycle that turns a text-predicting model into an agent that gets things done. ai-agentsreacttool-usereasoningloops https://learn.aathan.in/docs/retrieval-augmented-generation https://learn.aathan.in/docs/retrieval-augmented-generation Thu, 16 Jul 2026 11:10:00 GMT Ground an LLM in your own data by retrieving relevant context and feeding it into the prompt — the standard cure for hallucination and stale knowledge. ai-agentsragembeddingsvector-searchretrieval https://learn.aathan.in/docs/model-context-protocol https://learn.aathan.in/docs/model-context-protocol Thu, 16 Jul 2026 11:00:00 GMT The open standard that lets AI agents plug into tools and data — the "USB-C port for AI". ai-agentsmcptoolsintegrationsanthropic https://learn.aathan.in/docs/agents-md-files https://learn.aathan.in/docs/agents-md-files Thu, 16 Jul 2026 10:10:00 GMT A practical guide to AGENTS.md — the plain-Markdown, cross-tool file that tells coding agents how to build, test, and follow conventions in your repo, with a full example and monorepo nesting rules. agents-mdai-agentsopen-standardconventionsmonorepo https://learn.aathan.in/docs/skill-md-files https://learn.aathan.in/docs/skill-md-files Thu, 16 Jul 2026 10:05:00 GMT A deep dive into the SKILL.md format — YAML frontmatter fields, directory layout, bundled scripts, progressive disclosure, and invocation control, with copy-pasteable examples. claudeskillsskill-mdai-agentsllm https://learn.aathan.in/docs/agent-skills-overview https://learn.aathan.in/docs/agent-skills-overview Thu, 16 Jul 2026 10:00:00 GMT What "skills" mean for AI coding agents, how SKILL.md and AGENTS.md differ, and when to reach for each. ai-agentsskillsagents-mdclaudellm https://learn.aathan.in/docs/bellman-ford https://learn.aathan.in/docs/bellman-ford Thu, 16 Jul 2026 09:50:00 GMT The shortest-path algorithm that handles negative edge weights — and detects negative cycles. algorithmsgraphsshortest-pathbellman-ford https://learn.aathan.in/docs/a-star https://learn.aathan.in/docs/a-star Thu, 16 Jul 2026 09:40:00 GMT Dijkstra plus a heuristic — the point-to-point pathfinder behind game AI and map routing. algorithmsgraphsshortest-patha-starheuristics https://learn.aathan.in/docs/bidirectional-dijkstra https://learn.aathan.in/docs/bidirectional-dijkstra Thu, 16 Jul 2026 09:30:00 GMT Search from both ends at once and meet in the middle — often dramatically faster for single-pair queries. algorithmsgraphsshortest-pathdijkstra https://learn.aathan.in/docs/dijkstra https://learn.aathan.in/docs/dijkstra Thu, 16 Jul 2026 09:20:00 GMT The workhorse of shortest-path finding on weighted graphs — how it works, why it's correct, and a full worked example. algorithmsgraphsshortest-pathdijkstra https://learn.aathan.in/docs/bfs-shortest-path https://learn.aathan.in/docs/bfs-shortest-path Thu, 16 Jul 2026 09:10:00 GMT Breadth-first search finds the fewest-hops path, and it's the foundation every weighted algorithm builds on. algorithmsgraphsshortest-pathbfs https://learn.aathan.in/docs/shortest-paths-overview https://learn.aathan.in/docs/shortest-paths-overview Thu, 16 Jul 2026 09:00:00 GMT The shortest-path problem, the family of algorithms that solve it, and how to pick the right one. algorithmsgraphsshortest-path https://learn.aathan.in/docs/how-to-write-a-design-md https://learn.aathan.in/docs/how-to-write-a-design-md Wed, 15 Jul 2026 15:00:00 GMT A detailed guide to authoring a design.md — the single source of truth for a product's visual and interaction system. designdocumentationdesign-systems https://learn.aathan.in/docs/eigenvalues https://learn.aathan.in/docs/eigenvalues Wed, 15 Jul 2026 08:10:00 GMT What eigenvalues actually mean, how to compute them by hand, and worked 2×2 and 3×3 examples. linear-algebraeigenvalues https://learn.aathan.in/docs/getting-started https://learn.aathan.in/docs/getting-started Wed, 15 Jul 2026 07:20:00 GMT What this space is and how it's organized. meta https://learn.aathan.in/docs/react-hooks https://learn.aathan.in/docs/react-hooks Wed, 15 Jul 2026 07:20:00 GMT Quick reference for the hooks I reach for most. reacthooks