Owned intelligence
Build inspectable local NLP, retrieval, routing and learning components before depending on remote black boxes.
Cortex Engine · owned intelligence · continuously reviewed
an engineering lab for intelligence that can explain itself.
CortexLab connects technical publishing, model education, adaptive interviews, applied projects, research experiments and a first-party assistant that exposes confidence, grounding and response strategy instead of hiding uncertainty.
no external model api for core cortex chathuman reviewed teaching loopmdp + q-value policy learningcontext-aware project and article intelligence
cortex runtimelocal
01 / understandintent + neural routinggreeting · research · debug · interview · learning
02 / retrievebm25 + semantic vectors + graphlexical · semantic · graph · memory · reviewed teaching
03 / decidecalibration + policyevidence agreement · confidence · mdp state · q-values
04 / improvememory + feedback + reviewvisible memory · reward signal · reviewed teaching
Cortex local intelligenceNO EXTERNAL MODEL API
intent + neural routingtf-idf + bm25context groundingconfidence scoremdp policyq-value feedbacknaturalness profileparaphrase depthhuman teaching review
12K+interview question architecture
12learning tracks
20model dossiers
6engineering systems
6research tracks
Cortex Academy
learn the whole system not isolated definitions.
Every path moves through intuition, implementation, evaluation, failure analysis and a project. Cortex can then turn the track into a study plan or quiz you against the same knowledge.
01
Foundation → Intermediate
→02AI Engineering Foundations
Build the software and data foundations required before adding advanced AI components.
Intermediate
→03Machine Learning Core
Move from intuition to evaluated supervised and unsupervised learning systems.
Intermediate → Advanced
→04Deep Learning Systems
Understand neural networks from optimization to deployment and monitoring.
Advanced
→05NLP & Retrieval Engineering
Build search and language systems from deterministic NLP to semantic retrieval.
Advanced
→06Reinforcement Learning & Decision Systems
Learn MDPs, value methods, policy learning and safe decision optimization.
Advanced
→LLM & Agent Architecture
Study modern agent patterns without hiding engineering behind API calls.
Model Atlas
know why the model is used and where it breaks.
Each dossier includes the mental model, core math, best-fit problems, limitations, evaluation metrics and production checklist.
Linear models
open model atlasLogistic Regression
Learn a weighted score then pass it through a sigmoid to obtain a probability.
Tree ensemblesRandom Forest
Many trees vote so individual tree variance is averaged away.
Boosted treesGradient Boosting
Each new weak learner focuses on what the current ensemble still gets wrong.
ClusteringK-Means
Move centroids and assignments until within-cluster distance stops improving.
Dimensionality reductionPCA
Find the directions through the data cloud that preserve the most variance.
Neural networksMultilayer Perceptron
Repeatedly transform features into representations that make the target easier to separate.
Neural networksConvolutional Neural Network
Small learned filters scan local neighborhoods then combine them into higher-level features.
Sequence modelsTransformer
Each token dynamically decides which other tokens matter for its representation.
Engineering portfolio
all projects →systems that can be questioned from the inside.
01
Predictive ML system
Customer Churn Intelligence
An end-to-end customer-retention system that converts behavioral, subscription and support signals into calibrated churn risk and human-readable intervention guidance.
Logistic regressionRandom forestGradient boostingCalibration curvesCost-sensitive thresholding
Advanced buildopen engineering dossier →
02
First-party AI architecture
Cortex Local Intelligence Engine
The local NLP, retrieval, confidence, MDP-state and reinforcement-feedback engine powering Ask Cortex without an external generative-model API.
TF-IDFCosine similarityBM25Rule/feature intent routingContextual policy selection
Active R&Dopen engineering dossier →
03
Personalization system
Hybrid Recommendation Engine
A recommendation stack combining content similarity, behavioral events, skill gaps and exploration so learning recommendations remain useful even during cold start.
TF-IDF/content embeddingsCosine similarityWeighted implicit feedbackMMR-style diversificationContextual bandit exploration
Design + prototypeopen engineering dossier →
04
Assessment intelligence
Adaptive Interview Coach
A technical interview engine that samples from large domain banks, adapts difficulty, tracks weak concepts and schedules targeted review.
Weighted samplingBayesian-style mastery estimatesElo-inspired difficulty updatesSpaced repetitionContextual selection
Production foundationopen engineering dossier →
Research Observatory
advanced only earns a place when evidence survives.
CortexLab research treats complexity as a cost. Every idea needs a baseline, evaluation protocol, threat model, rollback path and evidence that would make us reject it.
Confidence-aware AI
Every answer should carry evidence quality, retrieval coverage and uncertainty signals.
Human-in-the-loop learning
Corrections become candidates with provenance and review rather than instant truth.
Decision optimization
Use MDPs, bandits and Q-value policies where sequential or online choices genuinely exist.
Knowledge systems
Treat articles, projects, models, interview questions and concepts as a connected graph.
Evaluation first
Promote an advanced model only after it beats a simpler baseline on meaningful metrics.
Active innovation
innovation lab →experiments connected to real product behavior.
01
Interactive model improvement
Human Teaching Loop
When Cortex is uncertain, it asks the user to verify or teach the correct explanation. Approved corrections become reusable knowledge with provenance.
Implemented foundationexplore →
02Reinforcement learning
MDP Response Orchestrator
Models response generation strategy as a decision process where intent, complexity, knowledge confidence and conversation state form the state representation.
Implemented foundationexplore →
03Structured knowledge
Knowledge Graph Learning
Connects topics, prerequisites, projects, innovations, blog posts and interview concepts so Cortex can explain relationships and learning paths.
Architecture trackexplore →
04Trainable NLP
Neural Intent Router
A planned small neural classifier that will replace parts of the rule router using locally trained examples while retaining deterministic fallbacks.
Research roadmapexplore →
05Optimization research
Autonomous SEO Lab
A guarded SEO research environment that proposes and tests improvements without allowing an optimizer to silently rewrite production content.
Active R&Dexplore →
Inside Cortex
a visible intelligence stack.
No single trick is called intelligence. Routing, retrieval, confidence, decision policy, learning and human review are separate layers that can be inspected and improved.
01
language
normalize · tokenize · synonym expansion · deterministic and neural intent
02
retrieval
bm25 · tf-idf · local dense vectors · knowledge graph · context scope · memory · approved teachings
03
confidence
lexical-semantic agreement · graph support · source margin · coverage · low-confidence gate
04
decision
intent × topic × difficulty × calibrated confidence becomes a response-policy state
05
response
visible plan · tutor · blueprint · debug · interview · research · socratic modes
06
learning
explicit feedback updates policy · reviewed corrections update knowledge · learners control long-term memory
07
style
paraphrase depth · naturalness profile · technical depth · readability
08
governance
provenance · admin review · audit trail · uncertainty disclosure
Publishing intelligence
knowledge library →articles are not dead pages.
Prompt Lab
response quality starts with a better contract.
Control intent, context, audience, depth, uncertainty rules, output shape and paraphrase style. Prompt engineering is treated as product interface design rather than magic wording.
cortex prompt contract
intent: explain\ncontext: model:xgboost\naudience: ml student\ndepth: deep\ninclude: tradeoffs + metrics + failure modes\nconfidence: expose\nstyle: conversational\nparaphrase: 3/4The larger idea
publishing becomes knowledge. knowledge becomes learning. learning creates signals. signals improve the system.
CortexLab is designed so every useful artifact can feed another surface without losing provenance. Projects teach. Articles become context. Interview mistakes reveal weak topics. Human corrections become reviewed knowledge. Research can promote better policies only after evaluation.
see platform architecture →Personal learning operating system
one profile connects lessons models projects interviews and cortex.
Signed-in learners can now persist Academy progress and learning goals. Recommendations use those signals to connect the next lesson with relevant model dossiers, applied projects and research directions.
learn
lesson-level Academy modules with labs, checks, failure modes and deliverables.
enter academy →practice
adaptive interviews and technical games produce evidence about weak and strong topics.
practice →build
project dossiers connect theory to architecture, data, evaluation and production decisions.
open projects →reflect
Cortex uses saved goals and contextual knowledge to tailor examples and next-step recommendations.
open learning OS →Knowledge discovery
search across the whole CortexLab graph.
One query can surface an Academy lesson, model dossier, project, innovation, research idea or published article. Search is no longer isolated to the blog.
01queryreinforcement learning
02lessonQ-learning
03modelMarkov Decision Process
04projectAdaptive Interview Coach
05researchDecision optimization
06cortexgrounded explanation
start with one question
open cortex →make cortex prove what it knows.
ask deeply. change the response style. inspect confidence. correct it when it is wrong. then continue learning from the same connected platform.