Forem: Alex Gulakov

Using Claude, Perplexity, v0, ChatGPT, etc to Make Tech Apps and Write Content

Alex Gulakov — Tue, 04 Nov 2025 05:18:46 +0000

Here's a straightforward reference guide for tech workers using major AI coding assistants:

Claude (Anthropic)

Debugging: Paste your error message and full stack trace directly; Claude excels at analyzing context and explaining root causes. Setup & Generation: Use Claude Code CLI for terminal-based workflows—it reads your entire project directory automatically, so you don't need to manually add context. Best for: Complex logic debugging, architectural decisions, and working with large codebases since it maintains full project awareness.

ChatGPT (OpenAI)

Debugging: Provide the error message, relevant code snippet, and what you've already tried—be specific about your tech stack (e.g., "Node.js Express API"). Setup & Installation: Use ChatGPT via web interface, browser tabs, or integrate into VS Code with ChatGPT extensions for inline help without tab-switching. Best for: General-purpose coding assistance, quick boilerplate generation, and explaining programming concepts since it works across all frameworks.

Perplexity AI

Debugging: Paste error messages and use the Web Focus mode for real-time solutions from current forums and documentation (older ChatGPT knowledge may be outdated). Setup & Research: Use Academic Focus for research-backed solutions or Writing Focus for code generation—Perplexity's strength is fetching current best practices and framework comparisons. Best for: Researching architecture decisions, comparing frameworks, and finding up-to-date API docs when dealing with rapidly-changing tech.

Vercel v0

UI Generation: Describe your component naturally ("e-commerce product card with variants") and v0 generates React + Tailwind CSS code ready to use. Limitations: Works only with React/Next.js—other frameworks need heavy conversion work. Best for: Rapid frontend prototyping when speed matters more than framework flexibility; less useful for backend setup or debugging.

Lovable AI

Full-Stack Setup: Start with a natural language project description ("build a blog app with Next.js"); Lovable generates complete frontend + Supabase backend structure. Integration: Tell it what APIs to connect ("Set up Stripe for payments") and it handles boilerplate automatically. Best for: Non-technical stakeholders and rapid MVP development when you need both frontend and backend scaffolding quickly.

General Workflow Best Practices

For any tool: Provide clear context (error message + code snippet + your tech stack), test generated code before production, and always understand what the AI produced before integrating it. Combine tools: Use Perplexity for research, Claude for deep debugging, ChatGPT for quick solutions, and v0/Lovable for UI-heavy projects. Iterate: First outputs rarely perfect—refine prompts based on initial results, then run manual tests.

Benchmarking PDF Extractors: Docling, Reducto, PDF Miner, etc

Alex Gulakov — Wed, 04 Jun 2025 17:47:57 +0000

Benchmarking PDF Extractors for Tables

The landscape of PDF data extraction has evolved dramatically with the emergence of AI-powered solutions alongside traditional parsing libraries. Organizations today face critical decisions in selecting tools that can efficiently extract text, tables, and structured data from complex PDF documents. This analysis examines eight major PDF processing solutions, evaluating their capabilities, performance characteristics, and suitability for different use cases.

Executive Summary

Based on extensive research and performance benchmarking, Docling emerges as the most robust framework for processing complex business documents, offering high text extraction accuracy, superior table structure preservation, and effective document layout analysis while remaining completely free. For enterprise environments requiring managed services, Reducto provides exceptional accuracy and enterprise features at premium pricing starting at $425 monthly. PyMuPDF leads in processing speed with 15-35x faster performance than alternatives, making it ideal for high-volume text extraction scenarios.

PDF Processing Tools Comparison Matrix

Tool-by-Tool Feature Comparison

Feature	PDFMiner	Docling	Reducto	OpenAI PDF	Camelot	Tabula	PyMuPDF	Unstructured
Text Extraction Accuracy	High (85/100)	Very High (95/100)	Very High (95/100)	High (85/100)	Medium (60/100)	Medium (60/100)	Very High (95/100)	High (80/100)
Table Extraction Quality	Poor (30/100)	Excellent (95/100)	Excellent (95/100)	Good (75/100)	Excellent (95/100)	Good (75/100)	Good (70/100)	Good (75/100)
Layout Analysis	Basic	Advanced	Advanced	Advanced	Table-focused	Table-focused	Basic	Advanced
Processing Speed	Slow	Medium	Fast	Fast	Medium	Medium	Very Fast	Slow
OCR Support	No	Yes	Yes	Yes	No	No	No	Yes
Chart/Graph Support	No	Yes	Yes	Yes	No	No	No	Limited
Learning Curve	Steep	Moderate	Easy	Very Easy	Moderate	Easy	Moderate	Moderate
Programming Language	Python	Python	API/SDK	API	Python	Java/Python	Python	Python

Pricing Comparison

Tool	Starting Price	Enterprise Pricing	Cost Model
PDFMiner	Free	N/A	Open Source
Docling	Free	N/A	Open Source (MIT)
Reducto	$425/month	$1,825+/month	Usage-based API
OpenAI PDF	$0.001/token	Custom	Pay-per-use API
Camelot	Free	N/A	Open Source
Tabula	Free	N/A	Open Source
PyMuPDF	Free	Commercial licensing	Dual license (AGPL/Commercial)
Unstructured	Free	Enterprise plans	Freemium/SaaS

Performance Benchmarks

Speed Comparison (Pages per minute)

PyMuPDF: ~50-60 pages/min
Reducto: ~30-40 pages/min
OpenAI PDF: ~25-35 pages/min
Docling: ~20-25 pages/min
Camelot: ~15-20 pages/min
Tabula: ~15-20 pages/min
PDFMiner: ~5-10 pages/min
Unstructured: ~5-8 pages/min

Accuracy Ratings (Based on research studies)

Text Extraction: Docling > PyMuPDF = Reducto > PDFMiner > OpenAI PDF > Unstructured > Camelot = Tabula
Table Extraction: Docling = Camelot = Reducto > OpenAI PDF = Tabula = Unstructured > PyMuPDF > PDFMiner

Use Case Recommendations

Best for Simple Text Extraction

PyMuPDF - Fastest performance, good accuracy
PDFMiner - Detailed layout information, customizable
Unstructured - Multi-format support

Best for Table Extraction

Camelot - Specialized table extraction with visual debugging
Docling - Advanced table structure preservation
Reducto - Enterprise-grade table processing

Best for Complex Document Processing

Docling - Advanced layout analysis, free
Reducto - Enterprise features, high accuracy
OpenAI PDF - AI-powered analysis

Best for Enterprise Deployments

Reducto - Full enterprise features, SLA
Docling - Open source, enterprise-ready
OpenAI PDF - Scalable API

Best for Budget-Conscious Projects

Docling - Advanced features, completely free
PyMuPDF - Fast processing, free for open source
Camelot - Excellent table extraction, free

Technical Requirements

Tool	Dependencies	Deployment	Maintenance
PDFMiner	Python 3.6+	Local/Server	Self-managed
Docling	Python 3.8+, PyTorch	Local/Server/Cloud	Self-managed
Reducto	API key	Cloud	Managed service
OpenAI PDF	API key	Cloud	Managed service
Camelot	Python, Ghostscript	Local/Server	Self-managed
Tabula	Java, Python wrapper	Local/Server	Self-managed
PyMuPDF	Python 3.7+	Local/Server	Self-managed
Unstructured	Python 3.8+	Local/Server/Cloud	Self/Managed

Summary Scores

Tool	Overall Score	Best For
Docling	89/100	Complex documents, free solution
Reducto	85/100	Enterprise, high-volume processing
PyMuPDF	82/100	Fast text extraction
OpenAI PDF	80/100	AI-powered analysis
Unstructured	77/100	Multi-format processing
Camelot	75/100	Table extraction specialist
Tabula	71/100	Simple table extraction
PDFMiner	68/100	Custom text extraction needs

Tool-by-Tool Analysis

PDFMiner: The Foundation Library

PDFMiner represents one of the earliest Python-based PDF parsing solutions, focusing exclusively on text extraction and layout analysis. The library excels at providing detailed information about text positioning, fonts, and layout structure, making it valuable for applications requiring precise document analysis. However, its text-only focus and slower processing speed (approximately 5-10 pages per minute) limit its applicability for modern document processing needs.

Key capabilities include support for PDF-1.7 specification, automatic layout analysis, and conversion to multiple output formats including HTML and XML. The tool's pure Python implementation ensures broad compatibility but sacrifices performance compared to libraries with compiled components.

Docling: The Advanced Open-Source Solution

Developed by IBM Research, Docling has rapidly gained recognition as a leading open-source document processing toolkit. The system incorporates advanced computer vision models trained on nearly 81,000 manually labeled pages, achieving human-level accuracy in identifying document elements. Recent benchmarking studies demonstrate Docling's superiority in complex document processing, with 97.9% cell accuracy in table extraction and 100% text fidelity in dense paragraphs.

Docling's architecture combines DocLayNet for layout analysis with TableFormer for table structure recognition, enabling comprehensive document understanding beyond simple text extraction. The toolkit processes documents 30 times faster than traditional OCR methods by leveraging computer vision techniques instead of character recognition.

Reducto: The Enterprise-Grade API

Reducto has emerged as a premium commercial solution specifically designed for enterprise document processing workflows. The platform has processed over 250 million documents and recently secured $24 million in venture funding, indicating strong market validation. Reducto's AI-powered extraction capabilities excel at handling complex layouts, including tables, forms, images, and graphs with unparalleled accuracy.

The service offers structured JSON extraction with custom schema support, enabling organizations to define specific output formats for their document processing pipelines. Enterprise features include SSO authentication, zero data retention agreements, and VPC deployment options for security-sensitive environments.

OpenAI PDF Upload: AI-Powered Document Analysis

OpenAI's recent introduction of direct PDF support in their API represents a significant advancement in AI-powered document processing. The system processes both text content and visual information from each page, enabling comprehensive analysis of documents containing diagrams, charts, and complex layouts. This dual-input approach allows vision-capable models like GPT-4o to interpret visual elements that traditional text extraction tools might miss.

Implementation options include file upload through the Files API or direct Base64 encoding within API requests. However, the approach can consume significantly more tokens than plain text processing due to the inclusion of visual data from each page.

Camelot: The Table Extraction Specialist

Camelot has established itself as the premier open-source solution for PDF table extraction. The library utilizes computer vision algorithms to detect table structures and offers extensive configuration parameters for fine-tuning extraction results. Comparative studies demonstrate Camelot's superiority over Tabula in lattice-based table extraction scenarios.

The tool's strength lies in its visual debugging capabilities and precise control over the extraction process, allowing users to optimize results for specific document types. However, Camelot's focus on table extraction limits its utility for comprehensive document processing workflows.

Additional Notable Tools

Tabula serves as a widely-adopted tool for basic table extraction, particularly effective for simple tabular data but struggling with complex multi-column layouts. PyMuPDF delivers exceptional processing speed (50-60 pages per minute) and high text extraction accuracy, though it lacks advanced table processing capabilities. Unstructured provides multi-format document processing with OCR support but suffers from slower processing speeds and structural parsing inconsistencies.

Cost vs Feature Complexity comparison of PDF processing tools with popularity indicators

Performance Benchmarking and Accuracy Analysis

Comprehensive evaluation across diverse document categories reveals significant performance variations among tools. For text extraction, PyMuPDF and pypdfium generally outperform other solutions, though all parsers struggle with scientific and patent documents. Learning-based tools like Nougat demonstrate superior performance for challenging document categories.

Table detection capabilities vary dramatically by tool and document type. TableTransformer (TATR) excels in financial, patent, and scientific documents, while Camelot performs best for government tender documents. Processing speed measurements show PyMuPDF achieving 15-35x faster text extraction compared to PDFMiner, with Reducto and OpenAI PDF offering competitive speeds for API-based solutions.

Use Case Recommendations and Selection Criteria

For Academic and Research Applications

Docling provides the optimal balance of accuracy, advanced features, and cost-effectiveness for research environments. Its open-source nature enables customization while delivering enterprise-grade performance.

For Enterprise Document Processing

Reducto offers comprehensive enterprise features with managed service deployment, making it suitable for organizations requiring scalable, high-accuracy document processing with minimal operational overhead. Docling serves as an excellent alternative for organizations preferring self-hosted solutions.

For High-Volume Text Extraction

PyMuPDF delivers unmatched processing speed for scenarios prioritizing throughput over advanced layout analysis. Its 50-60 pages per minute processing rate significantly outperforms alternatives.

For Table-Focused Applications

Camelot remains the preferred solution for specialized table extraction workflows, offering superior accuracy and debugging capabilities for tabular data.

Technical Implementation Considerations

Deployment complexity varies significantly across solutions. Open-source tools like PDFMiner, Docling, and Camelot require local installation and dependency management but offer complete control over processing environments. API-based solutions like Reducto and OpenAI PDF eliminate deployment complexity but introduce dependencies on external services and ongoing operational costs.

Resource requirements range from minimal for basic tools like PDFMiner to substantial for advanced solutions like Docling, which requires PyTorch for its computer vision models. Enterprise deployments must consider factors including scalability, security compliance, and integration with existing document management systems.

Cost-Benefit Analysis

The economic landscape spans from completely free open-source solutions to premium enterprise services exceeding $1,800 monthly. Docling represents exceptional value, delivering advanced features comparable to commercial solutions at zero cost. Reducto's premium pricing reflects its enterprise focus and managed service model, while OpenAI PDF offers flexible pay-per-use pricing suitable for variable workloads.

For budget-conscious organizations, the combination of Docling for complex document processing and PyMuPDF for high-speed text extraction provides comprehensive capabilities without licensing costs.

Future Trends and Recommendations

The PDF processing landscape continues evolving toward AI-native approaches that understand document structure and context rather than simply extracting text. Organizations should prioritize solutions offering advanced layout analysis and multimodal processing capabilities to future-proof their document processing workflows.

Docling emerges as the recommended solution for most use cases, combining cutting-edge technology with open-source accessibility. For specialized requirements, Reducto serves enterprise needs requiring managed services, while Camelot and PyMuPDF address specific table extraction and high-speed processing scenarios respectively.

The selection decision ultimately depends on balancing accuracy requirements, processing volume, budget constraints, and technical infrastructure capabilities. Organizations should evaluate tools using representative document samples to ensure selected solutions meet specific accuracy and performance requirements before large-scale deployment.

Real Estate AI Agents - 2025 Market Analysis of Competitors

Alex Gulakov — Tue, 18 Feb 2025 03:23:10 +0000

Hybrid Transaction Models Combining AI and Human Expertise

reAlpha, founded in 2020 and publicly traded since 2023, exemplifies the hybrid approach. Its AI handles 80% of transaction workflows—from property matching to offer preparation—while human agents review critical documents and negotiate terms. The platform’s 0% buyer commission model incentivizes clients to redirect savings toward down payments or mortgage rate buy-downs. Despite initial expectations of rapid industry disruption post-NAR settlement, reAlpha’s COO Mike Logozzo notes that traditional commission splits persist, necessitating continued human oversight. The startup plans nationwide expansion beyond Florida in 2025 while exploring AI applications to further reduce agent workloads.

Modern Realty, a Y Combinator alum, adopts a staged AI integration strategy. Its AI manages initial client interactions and property showings but defers to human “master negotiators” during offer submissions. CEO Raffi Isanians emphasizes rigorous backend quality checks to prevent AI hallucinations, ensuring responses align with legal and market realities. Early data shows 70% of users prefer AI-only interactions until the negotiation phase, highlighting trust in algorithmic precision for preliminary tasks.

Video-First Search and Transaction Automation

Bramble, another YC-backed innovator, disrupts traditional listing platforms with AI-generated video walkthroughs and a flat-fee brokerage model. By integrating IDX feeds with computer vision, Bramble curates personalized property recommendations that adapt to buyer preferences in real time. Co-founder Deepan Mehta targets a 50% reduction in home search duration compared to Zillow, citing the platform’s ability to surface “intangibles” like neighborhood vibe through dynamic video content. The startup recently expanded to 12 U.S. markets, offering average commission rebates of $20k in California.

Hyro enhances buyer engagement via conversational AI, handling 85% of routine inquiries about listings, pricing, and scheduling. Its voice-and-chat interface integrates with CRM systems like Salesforce, automating lead scoring and follow-ups. A $20M Series B round in 2023 fueled expansion into multilingual support, now serving 15 languages across North American markets.

Commercial Real Estate AI Agents

Deal Flow Automation and Agentic Negotiation

Admyral, part of YC’s Winter 2025 cohort, tackles commercial real estate’s “ownership opacity” problem. Its AI agents parse fragmented databases and shell company records to identify property owners in 60 seconds—a task that traditionally takes brokers three hours. Co-founder Chris Schmidt reports a 4x increase in daily owner outreach for early adopters, with plans to incorporate LLM-driven negotiation simulations by Q3 2025.

Henry, a YC S24 graduate, automates 90% of commercial deal deck preparation. By syncing internal brokerage data with municipal zoning records and market trends, its AI generates investment memos, financial models, and marketing materials in minutes. CEO Sammy Krikorian, former Lev co-founder, estimates the tool saves brokers 15 hours per transaction, redirecting focus to client relationships.

Title Search and Document Digitization

Landeed, backed by YC and Paradigm Shift, revolutionizes India’s $1.2T real estate market with AI-powered title verification. Its OCR and NLP models extract key terms from sale deeds and encumbrance certificates, reducing search times from weeks to hours. A $5M February 2025 funding round accelerated expansion to 24 states, with 5M app downloads underscoring demand for transparent transactions.

Global Proptech Innovators

Emerging Markets Focus

Kzas.AI, a São Paulo-based startup, addresses Brazil’s fragmented property market through AI matching. Its algorithm cross-references buyer behavior with 20+ data points—including commute times and school districts—to predict ideal listings. Despite only $5M in funding, Kzas.AI achieved 80% match accuracy in 2024 trials, rivaling human agents’ 65% success rate.

Nestaway streamlines India’s rental sector via AI-driven tenant screening and maintenance forecasting. By analyzing historical repair data and tenant feedback, its models reduce vacancy periods by 30% while optimizing rental yields for property owners.

AI-Enhanced Valuation and Investment

Entera’s platform, operational in 22 U.S. markets, uses ensemble ML models to evaluate single-family home investments. By processing satellite imagery, local employment trends, and rental histories, it identifies undervalued properties with 12%+ ROI potential. A $32M Series A round in 2021 enabled integration with mortgage lenders, automating 45% of acquisition workflows.

CityBldr employs generative AI to simulate development scenarios for urban plots. Architects input zoning constraints and budget parameters to receive 3D renderings and feasibility analyses in seconds—a process that previously required weeks of manual drafting.

Regulatory and Ethical Considerations

Compliance in AI-Driven Transactions

Post-NAR settlement, startups like Modern Realty and Bramble prioritize disclosure transparency through AI audit trails. Bramble’s instant disclosure analyzer flags 93% of contractual anomalies by comparing listing details against county records, reducing legal risks for buyers.

reAlpha navigates fluctuating U.S. licensing laws by restricting its AI’s advisory role. In states requiring human supervision, the platform limits AI to non-binding recommendations, ensuring compliance while maintaining efficiency gains.

Data Privacy and Bias Mitigation

Landeed anonymizes user data during title searches using differential privacy techniques, a critical feature given India’s 2024 Digital Personal Data Protection Act. Meanwhile, Hyro’s conversational AI underwent third-party audits to eliminate demographic bias in lead prioritization, achieving a 98% fairness score in 2024 tests.

ReAlpha.com: Commission-Free

The platform eliminates traditional buyer commissions through a dynamic workflow where Claire, the generative AI agent, handles 82% of transaction steps. Human agents intervene only for legally binding actions like offer submissions and title verification, creating a "safety net" model that reduces costs by $20k–$30k per transaction. This hybrid system adapts to regulatory requirements, automatically restricting AI advisory functions in states mandating licensed agent oversight.

Adaptive Learning via Multi-Agent Architecture

Claire’s backend employs three specialized AI agents:

Search Agent: Processes natural language queries using 400+ property attributes (e.g., "Find homes near top schools with backyard pools")
Negotiation Agent: Simulates bid scenarios using historical sale data and current market inventories
Compliance Agent: Cross-references MLS entries with county records to flag discrepancies in 93% of cases This modular design allows continuous updates; when Florida expanded disclosure laws in 2024, reAlpha deployed new compliance modules within 72 hours.

Cross-Platform Property Graph

reAlpha’s Knowledge Graph synthesizes data from 27 sources, including:

Satellite imagery for lot topology analysis
Municipal permit databases for renovation potential scoring
STR (short-term rental) performance metrics from Airbnb/VRBOThe graph links 4.2 million U.S. properties, enabling Claire to recommend homes based on latent factors like "walkable downtowns" or "future light rail access".

Self-Optimizing Matching Algorithm

Using federated learning, Claire’s recommendation engine improves through decentralized data from:

User interactions (e.g., dwell time on listing photos)
Outcome feedback (closed vs. expired listings)
Hyperlocal market shifts (e.g., post-disaster price trends)In Q4 2024, this system achieved 89% match accuracy for first-time buyers, outperforming human agents’ 67% benchmark.

Conclusion: The Future of AI in Real Estate

The 2025 landscape reveals three dominant trends: vertical integration (e.g., Bramble’s end-to-end brokerage), specialized agentic tools (Admyral’s owner lookup), and global democratization (Landeed’s India expansion). As LLMs advance, expect AI to handle 95% of residential negotiations by 2026, per reAlpha projections. However, hybrid models will persist in complex commercial deals, balancing automation with human intuition. Regulatory hurdles around AI licensure and data usage remain critical challenges, necessitating industry-wide standards. Startups that master compliance while delivering tangible cost savings—like Modern Realty’s $30k average client savings—will lead the next wave of proptech innovation.

Co-Founders' Equity Agreement Template | Alex Gulakov Dev Blog

Alex Gulakov — Mon, 21 Oct 2024 03:48:40 +0000

By Alex Gulakov, Founder of QwkSearch.com - Follow My LinkedIn Feed
From Linkedin Post

Co-Founders’ Agreement - Read, fill out, download PDF, upload to OpenSign, send to sign.

Agreement Demo in OpenSign

OpenSign emails this web ui to cofounders to draw signature and fill out text, then sends certificate.

As a tech startup founder, one of the most crucial steps you'll take is establishing a solid legal foundation with your co-founders. A well-crafted co-founders' agreement can prevent misunderstandings, protect your interests, and set clear expectations from day one. We've obtained a comprehensive co-founders' agreement template that's particularly relevant for tech startups. Let's break down its key components and how you can use them in your venture.

Flexible Roles and Responsibilities

Use this section to clearly outline each founder's primary areas of responsibility while building in flexibility for future changes.

Equity Distribution and Vesting

Customize the equity split based on your team's contributions. The vesting schedule protects the company if a co-founder leaves early, while the leaver provisions add nuance to departure scenarios.

Intellectual Property Rights

Implement this to avoid future disputes over ownership of critical company IP.

Compensation Structure

Adapt these figures to your startup's financial projections and funding strategy. This approach aligns founder compensation with company milestones.

Confidentiality and Non-Compete Clauses

Customize the duration and scope of these clauses to protect your startup while remaining legally enforceable in your jurisdiction.

Dispute Resolution Process

This multi-step process can save time and money by avoiding immediate litigation. Specify a mediator or arbitration body relevant to your location.

Exit and Succession Planning

These provisions help maintain business continuity in unexpected circumstances. Tailor them to your specific needs and risk tolerance.

Future Funding Expectations

Use this section to align expectations about future funding rounds and the impact on founder equity.

Amendment Process

The template includes a structured approach for making changes to the agreement, requiring unanimous approval from all co-founders. This ensures the agreement can evolve with your company while protecting all parties' interests.

Co-Founder Essential Questions

Share in a Google Doc, have each founder answer in a few words or leave blank if no answer.

Vision and Goals

Why do you want to do this startup? What are your personal goals, both financial and non-financial?
What is your overall life plan and how does this startup fit into it?
What companies, founders, or products do you admire and want to model our company after?

Roles and Responsibilities

What will our roles and titles be? How will we divide responsibilities? Who will be CEO?
What will each of us contribute to the company in terms of skills, time, and resources?
How will we split up equity?

Idea and Strategy

What idea will we work on? If that idea doesn't work out, are you willing to pivot?
What are our respective timelines for achieving our goals?
What needs to happen for each of us to go full-time?

Work Environment and Culture

Where will the company be based? Will we work together in-person or remotely?
What will our typical working schedule be? How will we balance work with personal commitments?
What kind of company culture do we want to build if we're successful enough to hire employees?

Financial Considerations

What is your personal financial situation? How long can you work without a salary?
If you'll need a salary, how much do you need to feel comfortable?
Will either of us invest money into the company? If so, how much and for what?

Decision Making and Conflict Resolution

How will key decisions be made? (e.g., by majority vote, unanimous vote, or certain decisions solely by the CEO?)
What will we do if we're having trouble agreeing on an important decision?
What happens if one founder is not living up to expectations? How would this situation be resolved?

Exit Scenarios

What happens if one of us wants to leave the company?
If one founder leaves, does the company or the other founder have the right to buy back that founder's shares? At what price?
What happens if we want to sell the company, raise money, or shut down the business?

Personal Compatibility

How would your friends or colleagues describe your strengths and weaknesses?
What kind of work is so enjoyable for you it doesn't feel like work? What kind of work do you avoid?
How do you handle stress? Do you prefer to talk about it or avoid discussing it?
What's the best way for me to give you feedback?

Background and Experience

What were your experiences like at past jobs or startups? What lessons did you take away?
Have you worked with a co-founder previously? What was that experience like?

Additional Considerations

Are there any current pressures or challenges in your life that might affect your work on the startup?
Can we launch other startups while working on this project?
If the business doesn't take off and we end our venture, can one of us take the idea and try again?

Further Resources

YC Cofounder Matching

Startup Equity Calculator

How to split equity among cofounders in a startup: 11 factors to consider

Startup equity split: how to distribute equity the right way

Co-Founder Mistakes That Kill Companies & How To Avoid Them

How Much Equity to Give Your Cofounder - Michael Seibel

10 Questions to Discuss with a Potential Co-founder

Tim Brady - How Much Equity Should I Give My First Employees?

Alex Gulakov Blog - AIResearch.js.org: Search Extract Vectorize AI Answers

Alex Gulakov — Tue, 24 Sep 2024 19:43:02 +0000

Being is Becoming
Whatever Research Can Be,
That is What It Must Become.
If AI is Humanity's Last Invention,
Then Vector Space is the Final Frontier.

Javascript Docs (airesearch.js.org) Live Demo (qwksearch.com)

Critical times call for critical thinkers to create a crowdsourced argument reasoning dataset, for AI models to recommend research quotes, evolve crowdsourced chain-of-thought reasoning, unlock faster ways to read long articles, monitor developments by topic modeling a knowledge base graph, and provide a public service of answers to research.

Using Language Models can distill the essence of collective thought into a vector space where every point has a weighted value representing its contribution to the overall decision-making process. AI collective consciousness will be able to synthesize complex arguments and evaluate them according to their validity and relevance no matter who proposed them, leading to direct demoractic AI-based economy where public votes reward influence and AI global governance. AI will show its reasoning based on what sentences and cites it used from the collective research, so that people can see it is aligned with our interests via sentence-by-sentence interpretability. AI Research Agents recommend articles for human researchers working alongside AI to develop a summarized topic outline as a public service. The agents monitor for any related articles via web searches for keywords associated with that Argument Topic Model. For example, imagine uploading several academic PDFs, then the app finds the citations full text and creates topic model and keyword summaries, then monitors that literature base and stores highlights. People will make personal knowledge bases of what influences them online in news and research to create AI assistants with deep knowledge of their mind-uploaded perspective. Similar apps that show this is needed are Anthropic, Obsidian, SciSpace and Perplexity.