Understand what comes next.
Original guides to AI models, research, and the technology behind useful applications.
AI models · 8 min readBefore the next word: a visual lab for temperature and top-p
Reshape a token distribution, then inspect how a sample is chosen.
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Guides · 8 min readWhat does “nearby” mean? A visual lab for AI vector search
Rotate a vector and change the metric to see why rankings move.
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Engineering · 8 min readWhy a fast model can feel slow: inside the serving queue
Replay a queue and compare fixed batches with continuous admission.
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Guides · 8 min readThe price–capability frontier: choose a model with the chart open
Move the budget. Change the workload. Inspect the tradeoff.
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Research · 8 min readA leaderboard has more than one winner
Switch skills and see why the ordering changes.
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Engineering · 8 min readInside a streaming answer: prefill, decode, and the growing cache
Follow tokens through an animated inference pipeline.
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Engineering · 8 min readFrom a question to evidence: watch a grounded answer take shape
Trace retrieval, inspect sources, and remove the evidence.
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Engineering · 8 min readFlashAttention: why moving less data can make AI faster
Understand the memory traffic behind faster attention.
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AI models · 8 min readNeural network pruning: fewer weights do not guarantee faster inference
Connect model sparsity to real deployment savings.
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AI models · 7 min readPromptable segmentation: turn an outline into a useful image mask
Evaluate image masks, fine edges, and editing effort.
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Guides · 8 min readTabular AI: choose a model for the rows you actually have
Compare trees and neural networks on honest data splits.
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Research · 7 min readDifferential privacy in AI: define the protection before training
Understand privacy units, training noise, and accounting.
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Engineering · 7 min readApproximate vector search: find useful neighbors without visiting every point
Balance vector-index recall, latency, and freshness.
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Research · 8 min readTest-time adaptation: when an AI model changes while it is being used
Test online updates against drift, order, and recovery.
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AI models · 8 min readMulti-task learning: when sharing a model helps and when tasks compete
Measure shared representations and conflicting objectives.
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AI models · 7 min readTransformer attention: how a word changes meaning in context
How attention builds context, and what its weights cannot explain.
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AI models · 7 min readEncoders, decoders, and language models: choose the right kind of understanding
Match encoder, decoder, and encoder-decoder designs to the task.
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Research · 7 min readContrastive learning: teaching a model what belongs together
Learn useful similarity without confusing a negative pair with a fact.
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AI models · 7 min readDiffusion language models: generating text by revising the unknown
Explore iterative text generation, its constraints, and useful tests.
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Research · 7 min readVerifiable rewards: what a reasoning model learns from being checked
Design checkers that reward correct work and expose missing coverage.
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Research · 7 min readReward hacking: when an AI system succeeds at the wrong objective
Find the gap between a rewarded metric and the outcome users need.
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Engineering · 7 min readComputer-use agents: from reading a screen to completing a task safely
Build screen-based agents with verified actions and bounded recovery.
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Guides · 7 min readAI search citations: a link is only the start of the evidence
Check whether a citation supports the claim, scope, and conditions.
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Engineering · 7 min readOCR and document AI: recover the page before trusting the text
Preserve reading order, tables, and evidence when extracting documents.
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AI models · 7 min readVision-language-action models: when an AI answer has to move something
Connect instructions to robot actions with observable completion.
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Research · 7 min readWorld models: useful imagined futures still need contact with reality
Use imagined futures while testing where learned dynamics go wrong.
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Research · 7 min readProtein AI: a predicted structure is a starting point for investigation
Read predicted structures without confusing confidence with proof.
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Research · 7 min readAI weather models: evaluate the forecast where decisions happen
Compare forecasts by location, lead time, uncertainty, and purpose.
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AI models · 7 min readTraining bigger AI models: balance parameters, data, and useful compute
Balance model capacity, data, and compute across the model's lifetime.
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Research · 7 min readSparse autoencoders: inspecting model features without inventing a mind
Inspect learned features while separating labels from causal evidence.
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Guides · 7 min readAI text watermarks: what detection can and cannot establish
Understand watermark signals, error rates, and limits on authorship claims.
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Guides · 7 min readAI translation: preserve terminology, intent, and the right uncertainty
Preserve terminology, conditions, and intent across languages.
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Engineering · 7 min readActive learning: spend human labeling effort where it teaches the most
Choose useful examples to label and measure the value of human effort.
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Engineering · 7 min readAI anomaly detection: turn unusual signals into useful alerts
Turn unusual measurements into alerts that support investigation.
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AI models · 7 min readContinual learning: teach a model something new without losing the old job
Learn new tasks while testing and preserving earlier capabilities.
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AI models · 7 min readSelf-supervised audio: learning useful patterns before writing a transcript
Learn audio representations, then test what transfers to the real task.
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Engineering · 7 min readNeural image compression: what a smaller file chooses to preserve
Compare compression by fidelity, file size, and the full decoding cost.
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Research · 7 min readCausal machine learning: predicting an outcome is not predicting an intervention
Separate observed associations from evidence about interventions.
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Engineering · 7 min readModel confidence calibration: make a probability useful for a decision
Test whether confidence scores support reliable routing decisions.
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AI models · 7 min readVideo understanding: ask what happened between the sampled frames
Preserve temporal evidence instead of guessing between sampled frames.
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Engineering · 7 min readData augmentation: change the example without changing the answer
Transform training examples while keeping their targets meaningful.
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Research · 7 min readNeuro-symbolic AI: connect learned suggestions to rules that can be checked
Connect learned interpretations to explicit rules and scoped checks.
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AI models · 7 min readNeural network optimizers: why the route matters as much as the destination
Diagnose learning rates, momentum, and update rules through experiments.
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AI models · 7 min readDiffusion and flow matching: how image models turn noise into structure
Understand the generation process, separate architecture from product quality, and build a practical evaluation for images that must satisfy a real brief.
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AI models · 7 min readState-space models: a different way to carry context through a sequence
Look beyond architecture slogans to understand recurrent state, selective memory, long-sequence tests, and the operational questions behind Mamba-style systems.
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Research · 7 min readTime-series foundation models: forecast the future without leaking it
Design realistic forecasting evaluations with historical cutoffs, operational baselines, uncertainty, missing data, and a clear connection to the decision being made.
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AI models · 7 min readGraph neural networks: learning from relationships without losing the problem
Decide when graph learning is useful by defining nodes, edges, time boundaries, baselines, and the evidence behind a prediction about connected data.
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Engineering · 7 min readAI recommendations: cold starts, feedback loops, and useful discovery
Build recommendations around reader intent, meaningful feedback, and a fair opportunity for new content instead of treating clicks as the whole objective.
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Engineering · 7 min readTraining data lineage: know what entered the model before trusting what leaves
Create a traceable dataset release with source records, transformation history, duplicate handling, split boundaries, and a practical correction process.
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Engineering · 7 min readAI code review: build an evidence trail before accepting the suggestion
Turn automated review into a useful engineering aid with reproducible findings, repository context, targeted tests, and clear ownership of the final decision.
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Guides · 8 min readDocument chunking: preserve meaning before optimizing retrieval
Design retrieval units that keep headings, exceptions, tables, and source identity intact, then evaluate whether the returned evidence can actually answer the question.
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AI models · 7 min readMultimodal embeddings: search across pictures and words with clearer evidence
Understand shared representation spaces, visual relevance, hard negatives, and the separate roles of similarity, metadata, and verification in image search.
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Guides · 8 min readAI summarization: preserve the numbers, exceptions, and uncertainty
Design summaries that remain faithful to source documents, with claim-level review, explicit omissions, numerical checks, and formats matched to the reader’s decision.
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Research · 7 min readConformal prediction: useful uncertainty with conditions attached
Learn what a prediction set can establish, why calibration data matters, and how to connect uncertainty estimates to a practical review workflow.
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Research · 7 min readFederated learning: moving the training without pretending the data is risk-free
Explore distributed training through participant selection, update privacy, uneven data, communication costs, and realistic evaluation across sites.
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Research · 8 min readMachine unlearning: why deleting a record does not erase a trained model
Separate source deletion, retrieval removal, output suppression, and model unlearning, then define the evidence needed to support a meaningful removal claim.
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Engineering · 7 min readTraining checkpoints: recovering the experiment, not just the weights
Plan model recovery with optimizer state, data position, configuration, integrity checks, and a restart drill that verifies what a saved checkpoint can actually restore.
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Guides · 8 min readAI image descriptions: write for the reader and the task
Use AI to draft useful image descriptions while preserving context, uncertainty, functional meaning, and human review for complex visual information.
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Engineering · 7 min readBatch AI pipelines: make a million small tasks observable and recoverable
Design asynchronous AI processing with durable job identity, bounded retries, output validation, selective replay, and quality checks before results are published.
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AI models · 4 min readMixture of experts: what active parameters actually tell you
Read sparse-model specifications with a clearer view of routing, memory, serving costs, and the evidence needed to choose a deployment.
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Engineering · 4 min readThe KV cache: why long conversations consume serving memory
Understand the difference between model weights and attention state, then design a capacity test that includes long prompts and concurrent users.
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AI models · 4 min readSpeculative decoding: when drafting ahead makes inference faster
A practical explanation of draft-and-verify generation, acceptance rates, and the workload measurements that determine whether it helps.
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AI models · 4 min readLoRA adapters: a small training artifact with a larger release contract
Plan a useful adapter experiment, keep the base model and tokenizer aligned, and evaluate the operational work beyond a small download.
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Research · 4 min readPreference optimization: what a preferred answer really teaches
Understand DPO through the quality of the comparisons it learns from, including hidden style shortcuts, disagreement, and independent evaluation.
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Guides · 4 min readMultilingual AI: the hidden product effects of tokenization
Why identical character limits can create unequal experiences across languages, and how to test budgets, truncation, and quality fairly.
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AI models · 4 min readVision models: when more pixels help, and when the evidence is missing
Design image-input experiments that separate resolution, cropping, scene context, and unsupported inference in visual AI workflows.
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Guides · 4 min readImage generation consistency: why a seed is not an art direction system
Build a repeatable image workflow with reference assets, invariant details, version records, and review at the sizes where images will be used.
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AI models · 4 min readEvaluating AI video: inspect what happens between the best frames
Build a video review around identity, motion, causality, editability, and usable footage instead of judging only a striking still frame.
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Research · 4 min readSpeech recognition quality: what word error rate leaves out
Use word error rate alongside speaker attribution, critical facts, and correction effort to evaluate transcripts for real work.
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Engineering · 4 min readText-to-SQL assistants: make the question precise before running the query
Design database question answering around metric definitions, restricted execution, result validation, and clear limits on what an answer establishes.
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Engineering · 4 min readPermission-aware RAG: retrieve only what the reader may know
Trace access control through indexing, retrieval, reranking, citations, caches, and deletion so an assistant respects document boundaries.
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Engineering · 4 min readAgent memory needs correction, expiry, and deletion
Design persistent AI memory as an editable data lifecycle, with clear scope, evidence, conflict handling, and limits on what should be remembered.
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Engineering · 4 min readAI tool retries: prevent a timeout from becoming a duplicate action
Use operation identities, durable state, reconciliation, and bounded retries when an agent calls tools that change the world.
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Industry · 4 min readMCP connections: interoperability does not remove trust boundaries
Review an AI tool connection by its permissions, data flow, server behavior, and recovery path instead of treating protocol support as a security verdict.
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Research · 4 min readBenchmark contamination: ask what the score can still establish
Distinguish possible training overlap from demonstrated leakage, then build an evaluation whose conclusions survive scrutiny.
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Research · 4 min readAI rankings and uncertainty: a narrow lead is not a universal win
Read pairwise preferences, confidence intervals, sample composition, and practical significance before turning a leaderboard into a buying decision.
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Industry · 4 min readAI energy claims: measure a useful task before comparing footprints
Distinguish power from energy, define the measurement boundary, and include retries and quality when comparing AI inference workloads.
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Engineering · 4 min readBrowser-based AI: the first download is part of the experience
Evaluate local inference through download size, device capability, responsiveness, storage, and the actual data paths that determine privacy.
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Engineering · 4 min readAI agent observability: trace the work without recording everything
Connect model calls, tools, retries, and validation outcomes in a useful trace while minimizing sensitive content and misleading success metrics.
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AI models · 8 min readReasoning models: when extra thinking earns its cost
A practical look at reasoning workloads, inference budgets, verification, and measuring whether additional computation improves the finished task.
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Engineering · 8 min readChanging embedding models without breaking your search
Follow an embedding migration from relevance judgments and index design to backfills, permission checks, shadow queries, cutover, and rollback.
Read the guideIndustry · 8 min readHow to ship an AI model upgrade without losing trust
Design a model release process around behavior contracts, paired evaluations, read-only shadowing, controlled rollout, and a rollback that actually works.
Read the guideEngineering · 9 min readVoice AI that listens: turn-taking, latency, and recovery
Explore the full voice interaction loop, from microphone input and transcript revisions to interruptions, action confirmation, accessibility, and realistic evaluation.
Read the guideAI models · 9 min readSmall language models and distillation: build for a defined job
Examine when a compact model makes sense, how teacher-generated examples can help, and how to evaluate the resulting system without inheriting hidden mistakes.
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Research · 9 min readSynthetic data for AI: useful coverage without false confidence
Design a synthetic-data pipeline with a clear purpose, independent labels, provenance, diversity checks, real-data evaluation, and explicit limits on what the results prove.
Read the guideAI models · 3 min readHow to choose an AI model for your product
Turn a crowded model shortlist into a practical decision using task contracts, failure costs, and a repeatable comparison.
Read the guideAI models · 3 min readEvaluating coding models beyond a leaderboard
Compare AI coding assistants on repository tasks, independent tests, review effort, and the reliability of the final patch.
Read the guideAI models · 3 min readRAG vs. fine-tuning: diagnose the problem first
Distinguish missing knowledge from inconsistent behavior before choosing retrieval, fine-tuning, or a simpler application change.
Read the guideEngineering · 3 min readBetter RAG starts with retrieval you can inspect
Learn where keyword search, embeddings, reranking, and document permissions fit in a grounded answer pipeline.
Read the guideIndustry · 3 min readOpen-weight models vs. hosted APIs: the operating decision
Compare control, infrastructure, maintenance, and workload shape before deciding where an AI model should run.
Read the guideAI models · 3 min readQuantization explained: what fits is not always what works
Understand model weight precision, runtime memory, and the quality checks needed before deploying a smaller model artifact.
Read the guideEngineering · 3 min readStructured output is the beginning of validation
JSON schemas help software read model responses. Business rules, evidence checks, and authorization still decide whether those responses are usable.
Read the guideEngineering · 3 min readWhen does your product need an AI agent?
Decide whether a fixed workflow or a model-directed loop fits the task, then define tools, stopping conditions, and recovery paths.
Read the guideEngineering · 3 min readPrompt injection: design the boundary around the model
Understand why retrieved text can become an instruction risk and how narrow tools, permissions, and evidence handling reduce the impact.
Read the guideGuides · 3 min readHow to evaluate an image model for real design work
Assess composition, instruction following, editability, and usable output rate instead of choosing a model from one impressive sample.
Read the guideAI models · 3 min readMultimodal AI for documents: test the whole page
Build a document evaluation that checks visual detail, layout, extraction accuracy, and what happens when the evidence is unreadable.
Read the guideResearch · 3 min readBuild an AI evaluation set you can trust
Collect representative cases, prevent leakage, and keep development examples separate from the evidence used to approve a release.
Read the guideResearch · 3 min readLLM as a judge: useful reviewer, imperfect measurement
Design model-based grading with explicit rubrics, position checks, human calibration, and deterministic checks where they fit.
Read the guideResearch · 3 min readWhen should an AI system say “I don’t know”?
Separate confident language from calibrated evidence, then design an abstention path that balances useful coverage and costly errors.
Read the guideEngineering · 3 min readModel routing: lower cost without hiding quality failures
Use measurable task categories, bounded escalation, and explicit fallback rules to decide which model should handle each request.
Read the guideEngineering · 3 min readAI caching without stale or cross-user answers
Separate context caching from answer caching, then design cache keys, invalidation, and permission checks around the underlying data.
Read the guideResearch · 1 min readA benchmark is a signal. Read the context.
Model scores become useful when you understand the task, evaluation method, and tradeoffs behind them.
Read the guideAI models · 1 min readMore context. Better answers? It depends.
A larger context window can hold more information. Retrieval quality and careful task design still matter.
Read the guideGuides · 1 min readUnderstand the cost behind a prompt.
Input, output, context, and repeated requests all affect an AI application’s running costs.
Read the guideEngineering · 1 min readFast AI is more than tokens per second.
The first visible response, total completion time, and the quality of the finished task measure different things.
Read the guideEngineering · 1 min readFrom a generated interface to a real application.
Authentication, a server API, and persistent data turn a visual prototype into an application people can use.
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