1import{ɵ as h,g as n,m as s,i,h as g,k as r,l as f,b as c,aT as w,c as d,M as v,j as p,N as m,af as b}from"./index-DlBDnqi_.js";const y=[{title:"Running an AI-Native Engineering Org",description:"An interactive learning guide to running an AI-native engineering organization â how planning, context gathering, code review, team composition, and metrics shift when agentic coding becomes the default.",date:"2026-06-06",timestamp:"",url:"/learn/ai-native-engineering-org"},{title:"Attribution Modeling, Visualized â Who Gets Credit for the Sale?",description:"Interactive explainer of marketing attribution models: switch between first-touch, last-touch, linear, time-decay, position-based (U/W/Z), and data-driven and watch how each splits the credit for one conversion across a customer journey â plus how to choose a model and why none is ever 100% accurate.",date:"2026-06-24",timestamp:"2026-06-24T12:00:00",url:"/learn/attribution-modeling"},{title:"How We Build Effective Agents â Barry Zhang, Anthropic",description:"Interactive breakdown of Barry Zhang's (Anthropic) AI Engineer Summit talk: the spectrum from features to workflows to agents, when NOT to build an agent, keeping agents simple (models using tools in a loop), thinking like your agent, and the open questions ahead.",date:"2026-06-30",timestamp:"2026-06-30T23:30:00",url:"/learn/building-effective-agents"},{title:"How I Deleted 95% of My Agent Skills and Got Better Results â Nick Nisi, WorkOS",description:"Interactive breakdown of Nick Nisi's (WorkOS) AI Engineer Summit talk: why your agent is lying to you, why deleting 95% of your skills made things better, the gotcha-only 553-line alternative to a 10,000-line doc-dump, and why every failure is a harness bug â not an agent bug.",date:"2026-07-02",timestamp:"2026-07-02T19:30:00",url:"/learn/deleted-95-percent-agent-skills"},{title:"Dynamic Subagents â Running Parallel Agents Reliably (LangChain Deep Agents)",description:"Interactive breakdown of LangChain's Dynamic Subagents: why orchestrating subagents in the agent's head breaks at scale, how writing code (the eval tool + task global) fixes it, and the six workflow patterns â Classify & Act, Fan Out & Synthesize, Adversarial Verification, Generate & Filter, Tournament, and Loop Until Done.",date:"2026-06-30",timestamp:"2026-06-30T23:55:00",url:"/learn/dynamic-subagents"},{title:"How Auth Brokers Work â Connecting Services with Stytch & Composio",description:"Interactive guide to auth middleware: how a service like Composio or Stytch brokers OAuth between apps â the four roles, the authorization-code flow, connected accounts, automatic token refresh, and consumer vs. provider architectures.",date:"2026-06-17",timestamp:"2026-06-17T12:00:00",url:"/learn/how-auth-brokers-work"},{title:"How GitHub Apps Work â Build a Bot You Can @Mention",description:"Interactive guide to GitHub Apps: app identity, JWTs, installation tokens, user OAuth, webhooks, and the full architecture for a bot that creates and reviews PRs when you @mention it on an issue.",date:"2026-06-11",timestamp:"2026-06-11T12:00:00",url:"/learn/how-github-apps-work"},{title:"How the Hermes Agent Works â Components & Architecture",description:"Interactive guide to Nous Research's open-source Hermes agent: one agent + one memory across every surface, its six capabilities (Connect, Remember, Schedule, Delegate, Search, Experiment), persistent skill-learning memory, isolated zero-context-cost subagents, gateway scheduling, and the five execution backends.",date:"2026-06-23",timestamp:"2026-06-23T12:00:00",url:"/learn/how-hermes-agent-works"},{title:"How Non-Engineers Learned to Ship with AI",description:"How Sentry's growth team migrated 2,500 CMS pages to Git and unlocked AI-native workflows so non-technical contributors could ship like engineers.",date:"2026-06-06",timestamp:"",url:"/learn/how-matt-learned-to-ship"},{title:"Jev's Architecture Unmasked: Probability Readouts Instead of Text",description:"Interactive breakdown of how Jev, TypeSafe's decision-classification model, was reverse-engineered from 10,000 black-box API calls - probability readouts, shared state with isolated questions, causal attention, listwise options, and outcome-trained calibration.",date:"2026-09-22",timestamp:"2026-09-22T14:30:00",url:"/learn/jev-architecture-unmasked"}
1,{title:"How is Linear So Fast? â Interactive Breakdown",description:"An interactive breakdown of how Linear achieves instant UI interactions â exploring sync engines, optimistic updates, and local-first architecture.",date:"2026-05-30",timestamp:"",url:"/learn/linear-speed-interactive"},{title:"MacBook M4 Pro as a Local AI Powerhouse",description:"How the MacBook Pro M4 Pro with 48GB unified memory can replace ChatGPT, Cursor, and Midjourney with fully local, private, offline AI tools.",date:"2026-06-06",timestamp:"2026-06-06T23:00:00",url:"/learn/macbook-m4-pro-local-ai"},{title:"Decoding LLM Model Names â Parameters, MoE, and Active Counts",description:"Demystify AI model naming conventions â what parameter counts mean, how Mixture-of-Experts changes active vs total parameters, and why model size alone doesn't predict performance.",date:"2026-05-30",timestamp:"",url:"/learn/model-names-explained"},{title:"The Private AI Business Blueprint",description:"How to build a $1,000/mo private AI business for law firms using RTX 5090, Ollama, RAG, and Open WebUI â hardware, software, business models, and reality checks.",date:"2026-06-01",timestamp:"",url:"/learn/private-ai-business-blueprint"},{title:"What Salesforce Learned from 20,000 Agent Deployments",description:"Key lessons from Salesforce's analysis of 20,000 enterprise Agentforce deployments â architecture, failure patterns, and what it takes to succeed at scale.",date:"2026-06-09",timestamp:"2026-06-09T14:00:00",url:"/learn/salesforce-20k-agent-lessons"},{title:"Skip-level One-on-ones: A Leader's Guide",description:"An interactive learning guide to running effective skip-level one-on-ones â surface blind spots, transfer social risk, and build trust across organizational layers.",date:"2026-06-03",timestamp:"",url:"/learn/skip-level-one-on-ones"},{title:"Building Self-Improving Tax Agents with Codex",description:"How to build tax agents that write, test, and improve their own code using OpenAI Codex â covering agent loops, self-evaluation, and iterative refinement patterns.",date:"2026-05-30",timestamp:"",url:"/learn/tax-agents-codex"},{title:"Temporal Architecture â and When You Need to Deploy It on GCP",description:"An interactive breakdown of how Temporal works: durable execution via an append-only event history, the four server services (frontend, history, matching, worker), how a workflow task flows through the system, pluggable persistence and history shards â then the practical question: when should you self-host it, and how do you actually run it on Google Cloud (GKE + Cloud SQL)?",date:"2026-07-19",timestamp:"2026-07-19T12:00:00",url:"/learn/temporal-architecture-gcp"},{title:"How to Train Your Agent: Building Reliable Agents with RL â Kyle Corbitt, OpenPipe",description:"Interactive breakdown of Kyle Corbitt's (OpenPipe) AI Engineer World's Fair talk: how a 14B open model trained with reinforcement learning (ART·E) beat o3 at email research â and the four-step recipe to do it yourself. Start with a prompted baseline, build a realistic environment, design a reward function, and survive reward hacking.",date:"2026-07-04",timestamp:"2026-07-04T12:00:00",url:"/learn/training-reliable-agents-with-rl"},{title:"TurboQuant: Vector Quantization for LLM Efficiency",description:"Learn how TurboQuant compresses high-dimensional vectors using two-stage quantization and random rotation, with real-world applications in LLM key-value cache compression.",date:"2026-06-12",timestamp:"2026-06-12T12:00:00",url:"/learn/turboquant-vector-quantization"}],k=(a,t)=>t.url;function x(a,t){if(a&1&&(n(0,"p",7),s(1),i()),a&2){const e=p().$implicit;r(),m(e.date)}}function A(a,t){if(a&1&&(n(0,"p",9),s(1),i()),a&2){const e=p().$implicit;r(),m(e.description)}}function _(a,t){if(a&1&&(n(0,"a",6),c(1,x,2,1,"p",7),n(2,"h3",8),s(3),i(),c(4,A,2,1,"p",9),n(5,"span",10),s(6,"Open â"),i()()),a&2){const e=t.$implicit;w("href",e.url,b),r(),d(e.date?1:-1),r(2),v(" ",e.title," "),r(),d(e.description?4:-1)}}const I={title:"Learning Resources â Dale Nguyen",meta:[{name:"description",content:"Interactive and standalone learning pages"}]},o=class o{constructor(){this.pages=[...y].sort((t,e)=>{const l=(e.date??"").localeCompare(t.date??"");return l!==0?l:(e.timestamp??"").localeCompare(t.timestamp??"")})}};o.ɵfac=function(e){return new(e||o)},o.ɵcmp=h({type:o,selectors:[["ng-component"]],decls:10,vars:0,consts:[[1,"bg-bg","py-16","px-4","sm:px-6","lg:px-8"],[1,"mx-auto","max-w-7xl"],[1,"text-center","mb-12"],[1,"text-4xl","font-bold","tracking-tight","text-fg","sm:text-5xl"],[1,"mt-4","max-w-2xl","mx-auto","text-lg","text-fg-muted"],[1,"grid","grid-cols-1","md:grid-cols-2","lg:grid-cols-3","gap-8"],[1,"group","relative","flex","flex-col","rounded-2xl","border","border-border","bg-surface","p-6","no-underline","transition","duration-300","hover:-translate-y-1","hover:border-accent","hover:shadow-glow",3,"href"],[1,"mb-2","text-xs","font-medium","text-fg-muted"],[1,"mb-3","text-xl","font-bold","text-fg","transition-colors","group-hover:text-accent"],[1,"line-clamp-3","text-fg-muted"],[1,"mt-auto","pt-4","text-sm","font-medium","text-accent"]],template:function(e,l){e&1&&(n(0,"div",0)(1,"div",1)(2,"div",2)(3,"h1",3),s(4,"Learning Resources"),i(),n(5,"p",4),s(6," Standalone interactive pages for exploring ideas and concepts. "),i()(),n(7,"div",5),g(8,_,7,4,"a",6,k),i()()()),e&2&&(r(8),f(l.pages))},encapsulation:2});let u=o;export{u as default,I as routeMeta};
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.