cogwarsgame.com is live, and with it v0.0.1: the first playable build of Cog Wars, a top-down twin-stick arcade shooter set in an omniverse woven from golden cogs. What the game is, what you can play today, and what is still coming.
A survey of AI methods for detecting spam and spoofed calls on narrowband 8 kHz μ-law (G.711) telephony. No published anti-spoofing equal-error-rate is measured on native 8 kHz audio, and the best acoustic results do not transfer to narrowband or to the dominant attack: voice changers on genuine voice. The defensible architecture is emergent from layer composition: a Boolean core of behavioral metadata plus STIR/SHAKEN, with acoustic detection demoted to a post-call signal.
A production deep-research agent can outperform human research teams when engineered around machine-native strengths: massive parallel fan-out, tireless tool-use loops, perfect recall, and deterministic replay. This is the architecture pillar — it owns the checkpointed graph runtime, the typed source/evidence/claim data model, role fan-out and fan-in, replay, adversarial source screening, human gates, and benchmarks — and cross-links to its companion for the retrieval and verification math.
A deep-research agent's core is a set of IR and verification algorithms: lexical (BM25), dense, and hybrid retrieval; reciprocal rank fusion and diversity-aware (MMR) selection; cross-encoder reranking; and atomic, NLI-based claim verification with counter-evidence. This guide owns the algorithm layer — retrieval architecture, fusion math, reranking, and verification — and cross-links to its companion architecture pillar for the graph runtime, data model, and governance.
Long-running AI agents exhaust even million-token context windows with file reads, tool outputs, and conversation history. This guide covers the move from naive truncation to adaptive, structure-aware pruning: dependency-graph tracking, hot/warm/cold memory tiers, relevance scoring, and delta-based file caching, with three concrete algorithm designs and a prioritized implementation roadmap.
Replacing JUCE with iPlug2 + Dear ImGui + NanoVG + Faust is architecturally viable but incomplete: the four-library stack covers roughly 60% of JUCE's functional surface. The remaining 40% — parameter lifecycle, MIDI, audio-to-GUI threading, and build/signing — must be built as a thin glue layer of eleven purpose-built components totaling about 24 developer-days. A subsystem-by-subsystem gap analysis, capability matrix, data-flow diagrams, and an implementation roadmap.
"Better than JUCE" is the wrong question — analog quality lives in the DSP algorithm layer, not the wrapper: anti-aliasing of nonlinearities (oversampling + ADAA), circuit-accurate Wave Digital Filters, and minimal voiced-by-ear algorithms. Plain JUCE gives the blocks, not the algorithms. Here is a hybrid FAUST (or Cmajor) + JUCE + chowdsp_wdf stack shipping VST3 + CLAP from one codebase, a license-verified free-DSP catalog, and the acceptance battery that makes agent-authored DSP trustworthy.
Optuna is a mature, MIT-licensed Python framework for hyperparameter optimization. This guide explains its core abstractions (studies, trials, samplers, pruning, storage) and ecosystem, then shows how to apply it to agentic AI pipelines: prompt tuning, retry-budget allocation, and multi-objective quality-vs-cost routing. It includes an honest cost guide — a Claude-class study costs $10–$600, not cents — and a framework for when Optuna is and isn't the right tool.
Chain-of-Thought, Tree-of-Thoughts, and Graph-of-Thoughts are three structure-enhanced reasoning paradigms. This article compares their design, performance, and cost using only within-paper benchmark numbers, then maps each onto LangGraph, AutoGen, and Anthropic's workflow patterns. The honest takeaway: structured reasoning pays off only when a task decomposes cleanly, has a cheap verifier, and runs on a model that is not already a strong reasoner — otherwise a simple workflow usually wins.
Robust AI agent skill systems are defined not by a single brilliant prompt but by structural contracts: explicit topology, typed state, deterministic checkpoints, and multi-layer verification that treats agent output as a claim to validate, not evidence to trust. This guide covers skill loading architecture, harness maturity, orchestration topology, reward design, incremental verification, subagent dispatch, and CI/CD evaluation gating across Claude Code, LangGraph, and AutoGen.