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https://abacusai-ipbxqn4h.manus.space/assets/index-BU9ZRUqk.js

js abacusai-ipbxqn4h.manus.space collected 2026-10-03 04:59:25 UTC 390,521 bytes, 302 lines download raw bytes

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30 *
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50 * @license lucide-react v0.453.0 - ISC
51 *
52 * This source code is licensed under the ISC license.
53 * See the LICENSE file in the root directory of this source tree.
54 */const ux=o=>
vendor: 7,460 bytes, lines 54-164
54o.replace(/([a-z0-9])([A-Z])/g,"$1-$2").toLowerCase(),Wh=(...o)=>o.filter((s,r,c)=>!!s&&c.indexOf(s)===r).join(" ");/**
55 * @license lucide-react v0.453.0 - ISC
56 *
57 * This source code is licensed under the ISC license.
58 * See the LICENSE file in the root directory of this source tree.
59 */var fx={xmlns:"http://www.w3.org/2000/svg",width:24,height:24,viewBox:"0 0 24 24",fill:"none",stroke:"currentColor",strokeWidth:2,strokeLinecap:"round",strokeLinejoin:"round"};/**
60 * @license lucide-react v0.453.0 - ISC
61 *
62 * This source code is licensed under the ISC license.
63 * See the LICENSE file in the root directory of this source tree.
64 */const dx=k.forwardRef(({color:o="currentColor",size:s=24,strokeWidth:r=2,absoluteStrokeWidth:c,className:f="",children:d,iconNode:g,...b},v)=>k.createElement("svg",{ref:v,...fx,width:s,height:s,stroke:o,strokeWidth:c?Number(r)*24/Number(s):r,className:Wh("lucide",f),...b},[...g.map(([p,x])=>k.createElement(p,x)),...Array.isArray(d)?d:[d]]));/**
65 * @license lucide-react v0.453.0 - ISC
66 *
67 * This source code is licensed under the ISC license.
68 * See the LICENSE file in the root directory of this source tree.
69 */const Qe=(o,s)=>{const r=k.forwardRef(({className:c,...f},d)=>k.createElement(dx,{ref:d,iconNode:s,className:Wh(`lucide-${ux(o)}`,c),...f}));return r.displayName=`${o}`,r};/**
70 * @license lucide-react v0.453.0 - ISC
71 *
72 * This source code is licensed under the ISC license.
73 * See the LICENSE file in the root directory of this source tree.
74 */const mx=Qe("Check",[["path",{d:"M20 6 9 17l-5-5",key:"1gmf2c"}]]);/**
75 * @license lucide-react v0.453.0 - ISC
76 *
77 * This source code is licensed under the ISC license.
78 * See the LICENSE file in the root directory of this source tree.
79 */const hx=Qe("ChevronLeft",[["path",{d:"m15 18-6-6 6-6",key:"1wnfg3"}]]);/**
80 * @license lucide-react v0.453.0 - ISC
81 *
82 * This source code is licensed under the ISC license.
83 * See the LICENSE file in the root directory of this source tree.
84 */const qo=Qe("ChevronRight",[["path",{d:"m9 18 6-6-6-6",key:"mthhwq"}]]);/**
85 * @license lucide-react v0.453.0 - ISC
86 *
87 * This source code is licensed under the ISC license.
88 * See the LICENSE file in the root directory of this source tree.
89 */const px=Qe("CircleAlert",[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["line",{x1:"12",x2:"12",y1:"8",y2:"12",key:"1pkeuh"}],["line",{x1:"12",x2:"12.01",y1:"16",y2:"16",key:"4dfq90"}]]);/**
90 * @license lucide-react v0.453.0 - ISC
91 *
92 * This source code is licensed under the ISC license.
93 * See the LICENSE file in the root directory of this source tree.
94 */const ps=Qe("CircleCheck",[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["path",{d:"m9 12 2 2 4-4",key:"dzmm74"}]]);/**
95 * @license lucide-react v0.453.0 - ISC
96 *
97 * This source code is licensed under the ISC license.
98 * See the LICENSE file in the root directory of this source tree.
99 */const $h=Qe("Circle",[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}]]);/**
100 * @license lucide-react v0.453.0 - ISC
101 *
102 * This source code is licensed under the ISC license.
103 * See the LICENSE file in the root directory of this source tree.
104 */const gx=Qe("Copy",[["rect",{width:"14",height:"14",x:"8",y:"8",rx:"2",ry:"2",key:"17jyea"}],["path",{d:"M4 16c-1.1 0-2-.9-2-2V4c0-1.1.9-2 2-2h10c1.1 0 2 .9 2 2",key:"zix9uf"}]]);/**
105 * @license lucide-react v0.453.0 - ISC
106 *
107 * This source code is licensed under the ISC license.
108 * See the LICENSE file in the root directory of this source tree.
109 */const Ph=Qe("ExternalLink",[["path",{d:"M15 3h6v6",key:"1q9fwt"}],["path",{d:"M10 14 21 3",key:"gplh6r"}],["path",{d:"M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6",key:"a6xqqp"}]]);/**
110 * @license lucide-react v0.453.0 - ISC
111 *
112 * This source code is licensed under the ISC license.
113 * See the LICENSE file in the root directory of this source tree.
114 */const yx=Qe("GripHorizontal",[["circle",{cx:"12",cy:"9",r:"1",key:"124mty"}],["circle",{cx:"19",cy:"9",r:"1",key:"1ruzo2"}],["circle",{cx:"5",cy:"9",r:"1",key:"1a8b28"}],["circle",{cx:"12",cy:"15",r:"1",key:"1e56xg"}],["circle",{cx:"19",cy:"15",r:"1",key:"1a92ep"}],["circle",{cx:"5",cy:"15",r:"1",key:"5r1jwy"}]]);/**
115 * @license lucide-react v0.453.0 - ISC
116 *
117 * This source code is licensed under the ISC license.
118 * See the LICENSE file in the root directory of this source tree.
119 */const vx=Qe("House",[["path",{d:"M15 21v-8a1 1 0 0 0-1-1h-4a1 1 0 0 0-1 1v8",key:"5wwlr5"}],["path",{d:"M3 10a2 2 0 0 1 .709-1.528l7-5.999a2 2 0 0 1 2.582 0l7 5.999A2 2 0 0 1 21 10v9a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2z",key:"1d0kgt"}]]);/**
120 * @license lucide-react v0.453.0 - ISC
121 *
122 * This source code is licensed under the ISC license.
123 * See the LICENSE file in the root directory of this source tree.
124 */const bx=Qe("Info",[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["path",{d:"M12 16v-4",key:"1dtifu"}],["path",{d:"M12 8h.01",key:"e9boi3"}]]);/**
125 * @license lucide-react v0.453.0 - ISC
126 *
127 * This source code is licensed under the ISC license.
128 * See the LICENSE file in the root directory of this source tree.
129 */const xx=Qe("Lightbulb",[["path",{d:"M15 14c.2-1 .7-1.7 1.5-2.5 1-.9 1.5-2.2 1.5-3.5A6 6 0 0 0 6 8c0 1 .2 2.2 1.5 3.5.7.7 1.3 1.5 1.5 2.5",key:"1gvzjb"}],["path",{d:"M9 18h6",key:"x1upvd"}],["path",{d:"M10 22h4",key:"ceow96"}]]);/**
130 * @license lucide-react v0.453.0 - ISC
131 *
132 * This source code is licensed under the ISC license.
133 * See the LICENSE file in the root directory of this source tree.
134 */const Sx=Qe("Menu",[["line",{x1:"4",x2:"20",y1:"12",y2:"12",key:"1e0a9i"}],["line",{x1:"4",x2:"20",y1:"6",y2:"6",key:"1owob3"}],["line",{x1:"4",x2:"20",y1:"18",y2:"18",key:"yk5zj1"}]]);/**
135 * @license lucide-react v0.453.0 - ISC
136 *
137 * This source code is licensed under the ISC license.
138 * See the LICENSE file in the root directory of this source tree.
139 */const wx=Qe("Minus",[["path",{d:"M5 12h14",key:"1ays0h"}]]);/**
140 * @license lucide-react v0.453.0 - ISC
141 *
142 * This source code is licensed under the ISC license.
143 * See the LICENSE file in the root directory of this source tree.
144 */const X0=Qe("Play",[["polygon",{points:"6 3 20 12 6 21 6 3",key:"1oa8hb"}]]);/**
145 * @license lucide-react v0.453.0 - ISC
146 *
147 * This source code is licensed under the ISC license.
148 * See the LICENSE file in the root directory of this source tree.
149 */const Ih=Qe("RotateCcw",[["path",{d:"M3 12a9 9 0 1 0 9-9 9.75 9.75 0 0 0-6.74 2.74L3 8",key:"1357e3"}],["path",{d:"M3 3v5h5",key:"1xhq8a"}]]);/**
150 * @license lucide-react v0.453.0 - ISC
151 *
152 * This source code is licensed under the ISC license.
153 * See the LICENSE file in the root directory of this source tree.
154 */const ep=Qe("Terminal",[["polyline",{points:"4 17 10 11 4 5",key:"akl6gq"}],["line",{x1:"12",x2:"20",y1:"19",y2:"19",key:"q2wloq"}]]);/**
155 * @license lucide-react v0.453.0 - ISC
156 *
157 * This source code is licensed under the ISC license.
158 * See the LICENSE file in the root directory of this source tree.
159 */const tp=Qe("TriangleAlert",[["path",{d:"m21.73 18-8-14a2 2 0 0 0-3.48 0l-8 14A2 2 0 0 0 4 21h16a2 2 0 0 0 1.73-3",key:"wmoenq"}],["path",{d:"M12 9v4",key:"juzpu7"}],["path",{d:"M12 17h.01",key:"p32p05"}]]);/**
160 * @license lucide-react v0.453.0 - ISC
161 *
162 * This source code is licensed under the ISC license.
163 * See the LICENSE file in the root directory of this source tree.
164 */const Tx=Qe("Trophy",[["path",{d:"M6 9H4.5a2.5 2.5 0 0 1 0-5H6",key:"17hqa7"}],["path",{d:"M18 9h1.5a2.5 2.5 0 0 0 0-5H18",key:"lmptdp"}],["path",{d:"M4 22h16",key:"57wxv0"}
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165 * @license lucide-react v0.453.0 - ISC
166 *
167 * This source code is licensed under the ISC license.
168 * See the LICENSE file in the root directory of this source tree.
169 */const Ex=Qe("TvMinimal",[["path",{d:"M7 21h10",key:"1b0cd5"}],["rect",{width:"20",height:"14",x:"2",y:"3",rx:"2",key:"48i651"}]]);/**
170 * @license lucide-react v0.453.0 - ISC
171 *
172 * This source code is licensed under the ISC license.
173 * See the LICENSE file in the root directory of this source tree.
174 */const np=Qe("X",[["path",{d:"M18 6 6 18",key:"1bl5f8"}],["path",{d:"m6 6 12 12",key:"d8bk6v"}]]);/**
175 * @license lucide-react v0.453.0 - ISC
176 *
177 * This source code is licensed under the ISC license.
178 * See the LICENSE file in the root directory of this source tree.
179 */const Ax=Qe("Zap",[["path",{d:"M4 14a1 1 0 0 1-.78-1.63l9.9-10.2a.5.5 0 0 1 .86.46l-1.92 6.02A1 1 0 0 0 13 10h7a1 1 0 0 1 .78 1.63l-9.9 10.2a.5.5 0 0 1-.86-.46l1.92-6.02A1 1 0 0 0 11 14z",key:"1xq2db"}]]);function kx(o,s){if(o instanceof RegExp)return{keys:!1,pattern:o};var r,c,f,d,g=[],b="",v=o.split("/");for(v[0]||v.shift();f=v.shift();)r=f[0],r==="*"?(g.push(r),b+=f[1]==="?"?"(?:/(.*))?":"/(.*)"):r===":"?(c=f.indexOf("?",1),d=f.indexOf(".",1),g.push(f.substring(1,~c?c:~d?d:f.length)),b+=~c&&!~d?"(?:/([^/]+?))?":"/([^/]+?)",~d&&(b+=(~c?"?":"")+"\\"+f.substring(d))):b+="/"+f;return{keys:g,pattern:new RegExp("^"+b+(s?"(?=$|/)":"/?$"),"i")}}var Jr={exports:{}},Fr={};/**
180 * @license React
181 * use-sync-external-store-shim.production.js
182 *
183 * Copyright (c) Meta Platforms, Inc. and affiliates.
184 *
185 * This source code is licensed under the MIT license found in the
186 * LICENSE file in the root directory of this source tree.
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Do",shortTitle:"Introduction",description:"Discover how one founder replaced an entire AI team — five real jobs, start to finish — for about ten times less than you'd expect. This is the power of Agent MapReduce.",badge:"strategy",badgeLabel:"Strategy",estimatedTime:"5 min",videoTimestamp:0,substeps:[{id:"1.1",title:"The Core Problem: AI Teams Are Expensive",content:"Running complex AI tasks with a single top-tier model like GPT-4 or Claude Opus is powerful — but it's also expensive and slow. Every token costs money, and long-context tasks can eat through budgets fast. Most founders assume they need the best model for everything. They're wrong.",callout:{type:"info",text:"Julia McCoy's ke
187y insight: You only need an expensive model for planning. The heavy lifting can be done by cheap, open-source workers."}},{id:"1.2",title:"The Solution: Agent MapReduce Architecture",content:"Agent MapReduce is a multi-agent architecture inspired by Google's MapReduce distributed computing paradigm. Instead of one model doing everything sequentially, you split the task: a smart planner breaks it into pieces, cheap workers execute in parallel, and the planner synthesizes the results. The result is near-frontier quality at a fraction of the cost.",callout:{type:"tip",text:"The name comes from the Map (distribute tasks to workers) and Reduce (aggregate results) pattern from distributed computing."}},{id:"1.3",title:"What You'll Build in This Tutorial",content:"By the end of this tutorial, you'll know how to use Abacus AI's Agent MapReduce system to: automatically fix accessibility bugs across a large codebase, review pull requests for quality issues, rank 50 job candidates from a Google Drive folder, build an equity research portfolio, and mine app store reviews for product insights — all with minimal cost.",callout:{type:"info",text:"The final demo — building a $10K stock portfolio in minutes — is held back until the end for maximum impact."}}]},{id:2,slug:"architecture",title:"Understanding the Agent MapReduce Architecture",shortTitle:"Architecture",description:"Deep dive into how the fusion architecture works: one elite planner model orchestrating a swarm of cheap open-source worker agents running in parallel.",badge:"agent",badgeLabel:"Agent",estimatedTime:"8 min",videoTimestamp:90,substeps:[{id:"2.1",title:"The Three-Layer System",content:"The architecture has three distinct layers. First, the User Layer — you write a natural language prompt describing your task. Second, the Planner Layer — a single smart (and more expensive) agent analyzes your request, asks clarifying questions if needed, and breaks the task into smaller independent pieces. Third, the Worker Layer — a swarm of cheap open-source models (DeepSeek, Gemma, Qwen) attack every piece simultaneously in parallel.",callout:{type:"info",text:"The planner uses an elite model for reasoning. The workers use cheap open-source models for execution. This is the key cost optimization."}},{id:"2.2",title:"The Map Phase: Distributing Work",content:"In the Map phase, the planner agent takes your complex task and identifies all the independent sub-tasks that can be worked on simultaneously. For a codebase review, it might identify 8 different UI areas. For resume ranking, it might assign 5 resumes per worker. The key insight: these sub-tasks must be truly independent — no worker should need another worker's output to do its job.",callout:{type:"tip",text:"Good task decomposition is the most important skill. If sub-tasks depend on each other, they can't run in parallel and you lose the speed advantage."}},{id:"2.3",title:"The Reduce Phase: Synthesizing Results",content:"Once all workers finish, the planner agent collects their outputs, merges the data, strips out duplicates, resolves conflicts, and formats the final result. This synthesis step is where the expensive model earns its cost — it needs to understand context, resolve ambiguities, and produce a coherent final output from potentially messy worker results."},{id:"2.4",title:"Cost Model: Why This Is 10x Cheaper",content:"Consider a task that takes 100,000 tokens total. With a single GPT-4 call at $0.03/1K tokens, that's $3.00. With Agent MapReduce, the planner uses ~5,000 tokens ($0.15), and 10 workers each use ~9,500 tokens of a cheap model at $0.001/1K tokens ($0.095 total). Total cost: ~$0.25. That's roughly a 12x cost reduction.",code:{language:"text",label:"Cost Comparison",code:`Single GPT-4 Call:
188  100,000 tokens × $0.03/1K = $3.00
189
190Agent MapReduce:
191  Planner (GPT-4): 5,000 tokens × $0.03/1K  = $0.15
192  10 Workers (DeepSeek): 95,000 tokens × $0.001/1K = $0.095
193  Total: $0.245
194
195Savings: ~12x cheaper`}}]},{id:3,slug:"setup",title:"Setting Up Abacus AI",shortTitle:"Setup",description:"Get access to Abacus AI's ChatLLM platform and configure it to use the Agent MapReduce system for your first multi-agent workflow.",badge:"setup",badgeLabel:"Setup",estimatedTime:"10 min",videoTimestamp:180,substeps:[{id:"3.1",title:"Accessing Abacus AI",content:"Abacus AI offers its multi-agent system through the ChatLLM platform. Navigate to chatllm.abacus.ai to get started. The platform provides access to both premium models for planning and a catalog of open-source models for worker tasks — all in one unified interface.",callout:{type:"tip",text:"Use Julia's referral link (chatllm.abacus.ai/fmj) to try the platform. It gives you access to the full Agent MapReduce feature set."}},{id:"3.2",title:"Connecting Your Data Sources",content:"Before running your first agent task, connect the data sources your agents will need. Abacus AI supports Google Drive, GitHub repositories, and direct file uploads. For the demos in this tutorial, you'll need a GitHub account (for the bug fix demo) and a Google Drive folder (for the resume ranking demo).",callout:{type:"info",text:"Connecting data sources is done once in the platform settings. Agents can then reference your connected sources in natural language prompts."}}
195,{id:"3.3",title:"Choosing Your Model Configuration",content:"In the Agent MapReduce settings, you configure two things: the Planner Model (choose a premium model — GPT-4o, Claude 3.5 Sonnet, or Gemini 1.5 Pro) and the Worker Model (choose a cheap open-source model — DeepSeek V2, Gemma 2, or Qwen 2.5). The platform handles all the orchestration automatically.",code:{language:"yaml",label:"Recommended Configuration",code:`planner_model: claude-3-5-sonnet  # or gpt-4o
196worker_model: deepseek-v2-chat     # cheap, fast, capable
197max_workers: 10                    # parallel execution limit
198synthesis_model: claude-3-5-sonnet # same as planner`}}]},{id:4,slug:"demo-bug-fixes",title:"Demo 1: Automated Bug Fixes on a Real GitHub Repo",shortTitle:"Demo: Bug Fixes",description:"Watch Agent MapReduce automatically identify and fix accessibility issues across the freeCodeCamp repository — a codebase with thousands of files — in minutes.",badge:"demo",badgeLabel:"Demo",estimatedTime:"12 min",videoTimestamp:300,substeps:[{id:"4.1",title:"The Prompt: Accessibility Audit at Scale",content:"The first demo targets the public freeCodeCamp/freeCodeCamp GitHub repository. The prompt instructs the planner to inspect the repo structure, identify 5-10 independent frontend/UI areas, assign each to a worker agent, and then merge findings to apply the safest improvements.",code:{language:"text",label:"Demo 1 Prompt",code:`Use the public GitHub repository 'freeCodeCamp/freeCodeCamp'.
1991. Inspect the repo structure.
2002. Identify 5-10 frontend/UI areas that can be reviewed independently.
2013. Assign each area to worker agents.
2024. Merge worker findings, remove duplicates, and apply only the
203   safest improvements, then fix the following:
204   * Missing 'aria-label's on icon-only buttons
205   * Missing or weak image 'alt' text
206   * Unlabeled form inputs
207   * Vague link or button text
208   * Dialogs/modals missing accessible names
209   * Loading or error states with unclear accessible text
210   * Repeated accessibility issues across similar components
2115. Return: How the work was split, a summary of the safest issues,
212   before/after snippets, and create PRs.`}},{id:"4.2",title:"How the Planner Decomposes the Task",content:"The planner agent first clones and analyzes the repository structure. It identifies 8 independent UI areas: the navigation components, form elements, modal dialogs, image galleries, button groups, error states, loading indicators, and link text. Each area is assigned to a separate worker agent that can operate independently.",callout:{type:"info",text:"The planner chose 8 areas because they're truly independent — a worker fixing nav components doesn't need to know what the modal worker is doing."}},{id:"4.3",title:"Parallel Execution and PR Creation",content:"All 8 workers run simultaneously. Each one scans its assigned area, identifies accessibility issues, and proposes fixes. The planner then merges the results, removes duplicate findings (e.g., if two workers both flagged the same shared component), and creates GitHub Pull Requests with the actual code changes — ready for human review.",callout:{type:"tip",text:"The system only applies 'safe' improvements — changes that are clearly correct and non-breaking. Ambiguous cases are flagged for human review rather than auto-fixed."}}]},{id:5,slug:"demo-code-review",title:"Demo 2: AI Code Review on Your Last 10 Pull Requests",shortTitle:"Demo: Code Review",description:"Let the agent team review your recent pull requests for bugs, security issues, missing tests, and code quality problems — with specific line-level suggestions.",badge:"demo",badgeLabel:"Demo",estimatedTime:"10 min",videoTimestamp:450,substeps:[{id:"5.1",title:"The Prompt: Comprehensive PR Review",content:"This demo connects to your GitHub account and reviews your last 10 pull requests. The prompt is designed to catch a wide range of issues: potential bugs, edge cases, inefficient code, missing tests, weak error handling, security issues, type-safety problems, unclear logic, and maintainability risks.",code:{language:"text",label:"Demo 2 Prompt",code:`Review my last 10 pull requests and help me improve c
212ode quality.
213Look for:
214- Potential bugs and edge cases
215- Inefficient code patterns
216- Missing tests
217- Weak error handling
218- Security issues
219- Type-safety problems
220- Unclear logic
221- Maintainability risks
222
223Flag issues, modify the code as per suggested changes, and create PRs.
224
225Return:
226- Overall code quality summary
227- PR-specific review comments
228- PRs with the actual changes`}},{id:"5.2",title:"Worker Assignment Strategy",content:"The planner assigns one worker per pull request (10 workers for 10 PRs). Each worker receives the full diff of its assigned PR and the repository context needed to understand the changes. Workers run in parallel, so all 10 PRs are reviewed simultaneously rather than sequentially.",callout:{type:"info",text:"This is the ideal use case for Agent MapReduce: N independent items that each require substantial analysis. 10 PRs × 5 min each = 50 min sequential vs. 5 min parallel."}},{id:"5.3",title:"Reading the Output",content:"The final output includes an overall code quality summary across all PRs, individual review comments for each PR with specific line references, and new GitHub PRs with the suggested code changes already applied. You can accept, modify, or reject each suggestion individually."}]},{id:6,slug:"demo-resume-ranking",title:"Demo 3: Rank 50 Job Candidates in Minutes",shortTitle:"Demo: Resume Ranking",description:"Upload 50 resumes to Google Drive and let the agent team rank every candidate against your job description — with strengths, weaknesses, and a final CSV report.",badge:"demo",badgeLabel:"Demo",estimatedTime:"12 min",videoTimestamp:600,substeps:[{id:"6.1",title:"The Prompt: Structured Candidate Evaluation",content:"This demo is particularly powerful for HR and recruiting workflows. The prompt connects to a Google Drive folder containing 50 resumes and evaluates each candidate against a detailed job description for a QA Engineer role.",code:{language:"text",label:"Demo 3 Prompt",code:`I want you to rank the 50 resumes from my Google Drive folder
229for a job opening. Provide a ranked list in a CSV file with
230strong and weak areas of each profile. Mask the names of
231the candidates.
232
233Role: QA Engineer
234- Focus: Blackbox testing (70%+ of responsibilities)
235- Remote role, must overlap with US Pacific Time until 10 AM PT
236- Requirements: 3+ years QA, automation tools (Selenium/Cypress/
237  Playwright), API testing (Postman)
238
239Evaluate for: competencies AND cultural fit with Abacus AI.
240
241[Google Drive Folder Link]`}},{id:"6.2",title:"Parallel Resume Processing",content:"The planner assigns 5 resumes per worker (10 workers × 5 resumes = 50 total). Each worker evaluates its batch against the job description, scores candidates on technical skills, experience match, and cultural fit indicators. The planner then normalizes scores across all workers (since different workers might have different scoring tendencies) and produces the final ranked list.",callout:{type:"warning",text:"Score normalization is critical when workers evaluate different subsets. Without it, a worker that scored generously would unfairly rank its candidates higher."}},{id:"6.3",title:"The CSV Output",content:"The final output is a structured CSV file with columns for rank, anonymized candidate ID, overall score, technical score, experience score, cultural fit score, top 3 strengths, and top 3 weaknesses. This gives hiring managers an actionable, data-driven shortlist in minutes instead of days.",code:{language:"csv",label:"Sample Output Format",code:`Rank,CandidateID,Overall,Technical,Experience,CulturalFit,Strengths,Weaknesses
2421,CAND-042,94,96,92,94,"Strong automation portfolio; Cypress+Playwright","No Postman certification"
2432,CAND-017,91,95,88,90,"3 blackbox-heavy roles; PST overlap confirmed","Limited API testing depth"
2443,CAND-031,89,88,91,88,"7 years QA; strong regression testing","Selenium-only; no Playwright"`}}]},{id:7,slug:"demo-equity-research",title:"Demo 4: Build a $10K Stock Portfolio with AI Research",shortTitle:"Demo: $10K Portfolio",description:"The showstopper demo: use Agent MapReduce to analyze the top 50 S&P 500 companies and build an optimized $10K portfolio with moderate risk — in minutes.",badge:"demo",badgeLabel:"Demo",estimatedTime:"15 min",videoTimestamp:780,substeps:[{id:"7.1",title:"The Prompt: Equity Research at Scale",content:"This is the demo Julia held back until the end — and for good reason. It demonstrates that Agent MapReduce isn't just for code tasks. The prompt asks the system to act as an equity researcher, analyze the top 50 S&P 500 companies, and recommend a 10-stock portfolio for a $10K investment over 3 years with moderate risk.",code:{language:"text",label:"Demo 4 Prompt",code:`Go through the list of top 50 companies out of S&P 500 stocks
245and act like an equity researcher to give me a list of 10 stocks
246portfolio to invest 10K USD to have maximum returns in 3 years
247with moderate risk appetite.
248
249Use Agent MapReduce.`}},{id:"7.2",title:"How Workers Analyze Companies",content:"The planner assigns 5 companies per worker (10 workers × 5 companies = 50 total). Each worker performs a mini equity analysis on its assigned companies: reviewing recent earnings, growth trajectory, competitive moat, analyst consensus, P/E ratio vs. sector average, and 3-year price target estimates. Workers return a structured analysis with a recommendation score.",callout:{type:"info",text:"Each worker is essentially acting as a junior analyst. The planner acts as the senior portfolio manager who reviews all analyses and makes the final allocation decisions."}},{id:"7.3",title:"Portfolio Construction and Allocation",content:"The planner synthesizes all 50 company analyses, selects the top 10 candidates based on risk-adjusted return potential, and determines the optimal allocation across the $10K. It considers sector diversification (no more than 30% in any single sector), correlation between holdings, and overall portfolio beta to ensure the moderate risk target is met."},{id:"7.4",title:"The Final Portfolio Output",content:"The output includes the 10 selected stocks with ticker symbols, the recommended dollar allocation for each, the investment thesis for each pick, key risks to monitor, and a rebalancing schedule. This is institutional-quality research produced in minutes — not weeks.",code:{language:"text",label:"Sample Portfolio Output",code:`Portfolio: $10,000 | Horizon: 3 Years | Risk: Moderate
250
251Ticker  Allocation  Thesis
252NVDA    $1,800      AI infrastructure dominance; data center growth
253MSFT    $1,500      Azure AI integration; enterprise moat
254AMZN    $1,200      AWS margin expansion; advertising growth
255META    $1,000      Reels monetization; AI-driven ad targeting
256GOOGL   $900        Search resilience; YouTube + Cloud
257AAPL    $800        Services margin; installed base loyalty
258JPM     $700        Rate environment; loan growth
259UNH     $600        Healthcare pricing power; Optum growth
260LLY     $700        GLP-1 pipeline; multi-year growth runway
261COST    $800        Membership model; recession-resistant
262
263Expected 3Y Return: +47% | Portfolio Beta: 0.92`}}]},{id:8,slug:"demo-review-mining",title:"Demo 5: Mine App Store Reviews for Product Insights",shortTitle:"Demo: Review Mining",description:"Analyze 100 Play Store reviews to extract the top 10 product improvements users are asking for — turning raw feedback into a prioritized feature roadmap.",badge:"demo",badgeLabel:"Demo",estimatedTime:"8 min",videoTimestamp:960,substeps:[{id:"8.1",title:"The Prompt: Customer Voice Analysis",content:"This demo shows how Agent MapReduce can be applied to customer research. The prompt analyzes the top 100 most recent Play Store reviews for the ChatLLM app and extracts the top 10 things users want built.",code:{language:"text",label:"Demo 5 Prompt",code:`Go through the top 100 Play Store reviews of ChatLLM
264from latest and analyse them to find out the top 10
265things to build.`}},{id:"8.2",title:"Parallel Review Analysis",content:"The planner assigns 10 reviews per worker (10 workers × 10 reviews = 100 total). Each worker identifies themes, sentiment, and specific feature requests in its batch. The planner then aggregates all themes, counts frequency, weights by sentiment intensity, and produces a ranked list of the top 10 most-requested improvements.",callout:{type:"tip",text:"This same approach works for any review source: G2, Trustpilot, Reddit threads, support tickets, or NPS survey responses. The prompt just changes."}},{id:"8.3",title:"From Reviews to Roadmap",content:"The final output is a prioritized list of 10 product improvements, each with: the feature description, the number of reviews mentioning it, representative quotes from actual users, and a suggested implementation complexity (low/medium/high). This is a complete product discovery session compressed into minutes."}]},{id:9,slug:"cost-analysis",title:"The Real Cost Breakdown: 10x Savings Explained",shortTitle:"Cost Analysis",description:"Understand exactly how Agent MapReduce achieves its cost advantage and learn how to calculate the savings for your specific use case.",badge:"results",badgeLabel:"Results",estimatedTime:"7 min",videoTimestamp:1110,substeps:[{id:"9.1",title:"Token Economics of Multi-Agent Systems",content:"The cost advantage comes from two sources: model selection (cheap workers vs. expensive all-in-one) and parallelism (time savings translate to operational cost savings). The planner model handles ~5-10% of total tokens but does the hardest cognitive work. Workers handle 90-95% of tokens but do repetitive, parallelizable tasks where cheaper models perform nearly as well.",callout:{type:"info",text:"Research shows that for well-defined, repetitive sub-tasks, models like DeepSeek V2 perform within 5-10% of GPT-4 quality at 1/30th the cost."}},{id:"9.2",title:"Calculating Your Savings",content:"To estimate your savings, identify your current task's total token count, the percentage that requires high-level reasoning (planner tokens) vs. repetitive execution (worker tokens), and the cost differential between your current model and a cheap open-source alternative.",code:{language:"python",label:"Savings Calculator",code:`# Agent MapReduce Savings Calculator
266def calculate_savings(
267    total_tokens: int,
268    planner_ratio: float = 0.08,  # 8% for planning
269    premium_cost_per_1k: float = 0.030,  # GPT-4o
270    worker_cost_per_1k: float = 0.001,   # DeepSeek V2
271) -> dict:
272    planner_tokens = total_tokens * planner_ratio
273    worker_tokens = total_tokens * (1 - planner_ratio)
274
275    single_model_cost = total_tokens / 1000 * premium_cost_per_1k
276    mapreduce_cost = (
277        planner_tokens / 1000 * premium_cost_per_1k +
278        worker_tokens / 1000 * worker_cost_per_1k
279    )
280
281    return {
282        'single_model_cost': f'\${single_model_cost:.2f}',
283        'mapreduce_cost': f'\${mapreduce_cost:.2f}',
284        'savings_multiplier': f'{single_model_cost / mapreduce_cost:.1f}x',
285    }
286
287# Example: 500K token task
288print(calculate_savings(500_000))
289# Output: single_model_cost=$15.00, mapreduce_cost=$1.33, savings=11.3x`}}]},{id:10,slug:"apply-to-your-business",title:"Applying Agent MapReduce to Your Business",shortTitle:"Apply It",description:"A practical framework for identifying which of your business tasks are ideal candidates for Agent MapReduce — and how to write effective prompts for each.",badge:"strategy",badgeLabel:"Strategy",estimatedTime:"10 min",videoTimestamp:1200,substeps:[{id:"10.1",title:"The MapReduce Suitability Test",content:"Not every task benefits from Agent MapReduce. The ideal task has three properties: it can be decomposed into N independent sub-tasks (no dependencies between workers), each sub-task requires substantial processing (not just a lookup), and the sub-tasks are homogeneous (workers can use the same instructions with different inputs).",callout:{type:"tip",text:"Ask yourself: 'Could I give this task to 10 interns simultaneously, each working on a different piece?' If yes, it's a MapReduce candidate."}},{id:"10.2",title:"High-Value Use Cases by Industry",content:"Agent MapReduce shines in any domain with large, repetitive analytical tasks. In marketing: analyze 1,000 competitor blog posts for content gaps. In sales: score 500 leads against your ICP simultaneously. In legal: review 200 contracts for non-standard clauses. In finance: analyze quarterly reports for 100 portfolio companies. In HR: screen 300 applications against job requirements.",callout:{type:"info",text:"The pattern is always the same: large batch of similar items + consistent evaluation criteria + need for synthesis = Agent MapReduce."}},{id:"10.3",title:"Writing Effective MapReduce Prompts",content:"The best prompts for Agent MapReduce follow a specific structure: (1) Define the data source clearly, (2) Specify the decomposition strategy (how to split the work), (3) Give workers clear evaluation criteria, (4) Specify the output format for each worker, and (5) Tell the planner how to synthesize results.",code:{language:"text",label:"Prompt Template",code:`DATA SOURCE: Describe where the data lives (GitHub repo,
290Google Drive folder, URL list, etc.)
291
292DECOMPOSITION: How to split the work
293(e.g., assign 5 items per worker, split by category)
294
295WORKER TASK: What each worker should do with its items
296(be specific about what to look for and how to score)
297
298OUTPUT FORMAT: What each worker should return
299(structured JSON, CSV row, markdown summary, etc.)
300
301SYNTHESIS: How to combine worker results
302(rank by score, deduplicate, create final report, etc.)`}},{id:"10.4",title:"Next Steps: Start Your First Agent Task",content:"You now have everything you need to start using Agent MapReduce in your business. Head to chatllm.abacus.ai, connect your data sources, and try one of the five demo prompts from this tutorial on your own data. Start with the resume ranking or review mining demos — they're the easiest to set up and deliver immediate, tangible value.",callout:{type:"tip",text:"Pro tip: Start with a small batch (10-20 items) to validate your prompt before scaling to hundreds. Iterate on the prompt until you're happy with the output quality, then scale up."}}]}],e2="7cYtuRMro_U",mp="This AI Built a $10K Stock Portfolio In Minutes",hp="Julia McCoy",t2=mt.length,n2={agent:{bg:"oklch(0.55 0.18 290 / 0.15)",text:"oklch(0.75 0.18 290)"},setup:{bg:"oklch(0.55 0.14 150 / 0.15)",text:"oklch(0.7 0.14 150)"},demo:{bg:"oklch(0.75 0.18 65 / 0.15)",text:"oklch(0.75 0.18 65)"},code:{bg:"oklch(0.55 0.18 220 / 0.15)",text:"oklch(0.7 0.18 220)"},strategy:{bg:"oklch(0.65 0.18 30 / 0.15)",text:"oklch(0.75 0.18 30)"},results:{bg:"oklch(0.65 0.18 90 / 0.15)",text:"oklch(0.78 0.18 90)"}};function a2({activeStep:o,completedSteps:s,onStepClick:r,isOpen:c,onClose:f}){const d=s.size,g=d/mt.length*100;return y.jsxs(y.Fragment,{children:[c&&y.jsx("div",{"data-loc":"client/src/components/Sidebar.tsx:38",className:"fixed inset-0 z-30 lg:hidden",style:{background:"oklch(0 0 0 / 0.6)"},onClick:f}),y.jsxs("aside",{"data-loc":"client/src/components/Sidebar.tsx:46",className:"fixed top-0 left-0 h-full z-40 flex flex-col transition-transform duration-300",style:{width:"280px",background:"oklch(0.13 0.008 260)",borderRight:"1px solid oklch(0.20 0.008 260)",transform:c?"translateX(0)":"translateX(-100%)"},children:[y.jsxs("div",{"data-loc":"client/src/components/Sidebar.tsx:56",className:"px-5 py-4 flex items-center gap-3",style:{borderBottom:"1px solid oklch(0.20 0.008 260)"},children:[y.jsx("img",{"data-loc":"client/src/components/Sidebar.tsx:60",src:"/manus-storage/logo-icon_dc8361e0.png",alt:"Abacus AI Tutorial",className:"w-8 h-8 object-contain"}),y.jsxs("div",{"data-loc":"client/src/components/Sidebar.tsx:65",className:"flex-1 min-w-0",children:[y.jsx("div",{"data-loc":"client/src/components/Sidebar.tsx:66",className:"text-xs font-semibold tracking-wider up
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