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Guillermo Rauch’s Engineering Principles

linkedin.com
Author: Guillermo Rauch
Date: November 2023

Some engineering principles I live by, learned over the years at Vercel:
✓ Make it work, make it right, make it fast
✓ Progressive disclosure of complexity
✓ Minimize the number of concepts & modes
✓ Most ‘flukes’ aren’t… your tech just sucks
✓ Feedback must be given to users instantly
✓ Maximize user exposure hours
✓ Demo your software frequently to fresh eyes
✓ Sweat every word of product copy you render
✓ You’re never done working on performance
✓ You’re never done. Software ages like milk, not wine
✓ Visualizing traces of time is the best way to optimize it
✓ Ship frequently and strive to build in public
✓ Errors must have globally unique codes & hyperlinks
✓ Red is not enough to signal “error” (8% of men have red-green color blindness)

Comments:

  • Performance Design (Peter Veentjer):

    • Performance-critical systems require initial designs meeting performance criteria.
    • Optimize incrementally around a performant core to avoid rewrites.

  • Additional Principles (Diego Peralta):

    • Build tools for repeated workflows.
    • Write replaceable, not extensible, code.
    • Adopt Documentation-Driven Development (DDD).
    • Open-source everything possible.
    • Prioritize the organization as the customer of development teams.

Quotes:

  • “Software ages like milk, not wine” – Guillermo Rauch
  • “Performance is integral to design; optimize later around a solid core” – Peter Veentjer
  • “Some programmers are 10x, others -1x; I’ve been both.” – Diego Peralta

References Captured1:

How I Write Code Using Cursor: A Review

comments arguingwithalgorithms.com
Author: Tom Yedwab
Date: October 25, 2024

  • Cursor Overview: Fork of VS Code with integrated AI features (tab completion, inline editing, chat sidebar, composer). Subscription grants advanced tab completion2.
  • Tab Completion:
    • Automates edits across files with suggestions triggered by Tab.
    • Efficient for boilerplate, refactoring, and contextually appropriate completions.
    • Issues: Irretrievable dismissed suggestions and occasional incorrect completions.
  • Editing Features:
    • Inline edits for small changes; chat/sidebar for larger, multi-file refactors.
    • .cursorrules file informs AI of project-specific standards.
    • Limitations: Context scope and cross-language consistency in monorepos.
  • Criticism:
    • AI reliance risks diminishing deep understanding of coding.
    • Privacy concerns with proprietary data in AI systems.

Tools and Links Captured3456789

Vector Embedding Performance and Indexing Techniques

comments linkedin.com
Author: Daniel Svonava

  • Flat Indexing: Simple and precise but computationally expensive for large datasets.
  • Locality-Sensitive Hashing (LSH): Groups vectors via hashing; improves efficiency but trades off some accuracy.
  • Inverted File Indexing (IVF): Clusters vectors with variations like IVF_FLAT, IVF_PQ, and IVF_SQ for specific speed-memory tradeoffs.
  • Disk-Based ANN (DiskANN): Graph-based, SSD-optimized indexing for scalability and reduced disk reads.
  • SPANN: Hybrid in-memory and disk-based approach for large-scale datasets, leveraging centroids and pruning.
  • Hierarchical Navigable Small World (HNSW): Hierarchical graph indexing offering real-time performance on large datasets.

Recommendations

  • Dataset Size and Precision:
    • Use Flat Indexing for small datasets and high precision.
    • Transition to IVF for balanced speed and accuracy as you scale.
    • Opt for DiskANN or SPANN for massive datasets using SSDs.
    • Leverage HNSW for real-time large dataset needs.

Notable Comments & Resources

  • Critiques on LSH: Often inaccurate for most cases; suitable for high dimensionality.
  • Data Change Speed: Dataset volatility impacts reindexing needs10.
  • Alternatives to Vector DBs: Nested hashes for variable-length embeddings11.
  • Space Complexity Discussion: Insights on libsql’s use of DiskANN12.

Suggested Further Reading

  • Superlinked’s guide to implementing and comparing vector indexes13.
  • Amazon blog on pgvector optimizations for embedding loading10.

21 Reflections on Software Engineering and Tech

linkedin.com
Author: Owain Lewis

  1. Good code can’t fix a bad product.
  2. “What if we do nothing?” is a powerful question.
  3. Be the most helpful person in the team.
  4. Act like a leader before you are one.
  5. Look for simple solutions to complex problems.
  6. Titles don’t mean as much as you think they do.
  7. Success is probability. Show up more often.
  8. Ask “Why?” more often. Questions create clarity.
  9. There is no “best” X. It’s all tradeoffs.
  10. You are rewarded for getting things done.
  11. Writing is how to clarify your thinking.
  12. Be someone you’d want to work with.
  13. Everything is a negotiation.
  14. Fear kills trust. Lack of trust kills culture.
  15. Side projects are a great way to learn.
  16. Understand the business, not just the tech.
  17. Estimates are guesses, not promises.
  18. Find a mentor or a coach (don’t struggle alone).
  19. When you can’t win, change the rules.
  20. Imposter syndrome never goes away.
  21. Be the go-to expert in something.

Key Comments and Additions

  • Andrea Petrone: Collaboration beats competition; great teams elevate contributions.
  • Ashleigh Tennent: Learn to communicate clearly with non-technical teams. Bridging gaps strengthens teams and products.
  • Gregor Ojstersek: Happy developers build great products.
  • Marija Stevanovic: Side projects encourage exploration and frequent pivots.
  • Francisco Manuel Soto Ramírez: There are no good or bad technologies, only tradeoffs.
  • Carlos Alegria: Avoid ambiguous terms like “value” when discussing goals.

LitServe: A New Way to Deploy Machine Learning Models

linkedin.com
Author: Banias Baabe

  • Introduction to LitServe:

    • Open-source serving engine for machine learning models, built on FastAPI14.
    • Features include:
      • GPU autoscaling.
      • Automatic Dockerization.
      • Batching and streaming support.
      • Claims of being 2x faster than using FastAPI directly.

  • Community Feedback and Features:

    • Simplifies deployment for small teams without DevOps expertise.
    • Questioned claims:
      • “2x faster than FastAPI”: Skepticism due to its foundation on FastAPI.
      • Typed interfaces: Missing, despite FastAPI compatibility.
      • Comparisons with alternatives (e.g., BentoML, Ray Serve) for scaling and workflows.
    • Concerns about scaling for complex pipelines, resource allocation, and production workflows.
    • Some developers believe FastAPI alone can achieve similar results without additional dependencies.

The State of LLM Operations or LLMOps: Why Everything is Hard (And That’s OK)

zenml.io
Author: Alex Strick van Linschoten
Date: November 4, 2024

  • LLM Challenges in Production: Early adoption reveals hurdles across the ML lifecycle, from pipelines to deployment.
  • Pipeline Complexity: Diverse input formats and evolving tools complicate building robust systems.
    • Example: Processing non-standard PDFs demands vision-language models.
  • Quality & Monitoring: Outputs vary due to probabilistic nature, creating reliability issues at scale.
    • Intel uses regular model recycling and automated testing to address drift.
  • Integration & Security: Embedding LLMs in production struggles with response accuracy vs. performance tradeoffs.
    • On-premises deployments complicate scaling and require secure vector management.
  • Emerging Solutions: Hugging Face TGI streamlines inference; RAG offers a promising architecture despite complexity.
  • Open Gaps: Tools for consistent outputs, quality monitoring, and seamless UI/UX integration remain immature.

Tools and Links Captured151617

Jobright.ai: AI-Enhanced Job Hunting Platform

jobright.ai

Key Features

  • AI Job Matching:

    • Matches jobs based on skills, not just titles.
    • Custom job alerts ensure early applications.
    • Filters out fake jobs for higher reliability.

  • Resume AI:

    • Generates ATS-compatible, professional-quality resumes in minutes. Tailors resumes to specific job descriptions with AI assistance.

  • Insider Connections:

    • Connects users with alumni, past colleagues, and hiring managers in target companies.
    • Offers custom templates for personalized outreach, increasing interview chances by 4X.

  • AI Copilot “Orion”:

    • Provides tailored job suggestions, company insights, and career coaching.

Additional Tools

  • AI Cover Letter Generator.
  • AI Job Tracker for managing applications.

Resources181920

The Evolution of SaaS Monetization: Kyle Poyar’s Framework

linkedin.com
Author: Kyle Poyar

The old rules for scaling subscription SaaS:

  • Charge based on seats
  • Charge more for premium features
  • Pay reps on first year bookings
  • Hand customers off to CSMs post-close
  • Get customers to commit upfront

The new rules for scaling outcome-as-a-service products:

  1. Charge based on units of work
  2. Don’t put gates in the way of adoption
  3. Pay reps on adoption (estimated/actual)
  4. Every team plays a role in customer success
  5. Invest in becoming predictable

Moving from selling access to selling work delivered isn’t a simple pricing change you can just announce in a press release. It’s a business model evolution that looks a lot like the shift from on-prem to SaaS in the first place.

Key Comments and Insights

  • Drew Teller (Labelbox): Seat-based pricing still dominant for collaborative SaaS due to simplicity and scalability. Usage-based pricing is volatile and dependent on market conditions.
  • Dave Scalera (Merkle): Even consumption models often tie back to ARR commitments, with predictable usage and renegotiation for higher tiers as demand grows.
  • Maja Voje: Advocates avoiding penalties for adoption; incentivize growth rather than gatekeeping.
  • Leah Tharin: Jokes about outdated sales tactics like offering merch for inflated ACV leads.
  • Allan Duarte: Curious about real-world examples of companies successfully transitioning to outcome-as-a-service.
  • Chris Hogan (HubSpot): Frames Poyar’s model as aspirational but relevant for SaaS evolution.
  • Michal Slavev: Seeks new metrics defining success for outcome-based services.

Tools and Resources21

linkedin.com
Author: Alexander Kondov

Key Recommendations

  • React & Next.js:
    • Use React for flexibility; marginal benefits in switching to newer frameworks.
    • Next.js preferred for SSR22; otherwise, scaffold with Vite23.
  • Styling:
    • Tailwind CSS24 favored for maintainability over BEM or styled components.
    • CSS modules recommended for developers disinterested in utility classes.
  • Schema Validation:
    • Zod25 for schema validation; infer types for single-source-of-truth approach.
  • Data Fetching:
    • React-query26 simplifies state management for network requests.
    • Discipline in implementation prevents over-reliance on library-specific patterns.
  • State Management:
    • Zustand27 for minimal complexity; avoids unnecessary global store overhead.
  • Component Library:
    • Shadcn28 for minimalist UI, built on Radix with strong accessibility.
  • Code Generation:
    • Generate network clients using OpenAPI specs with libraries like openapi-zod-client29.

Additional Suggestions from Comments

  • Alternatives:
    • Remix30 for SSR, citing web-native POST interactions (Justin Farrell).
    • DaisyUI31 for maintainable Tailwind-based UI (Justin Farrell).
    • Mantine32 for a comprehensive UI library (Xiao Hanyu).
  • Complementary Tools:
    • TanStack Router33 for type-safe routing (Mustafa Nuur, Gregory Zoeller).
    • React Hook Form34 for form handling, integrates well with Zod (Mustafa Nuur).
    • tRPC35 for input validation using Zod schemas (Franco Rodríguez Roura).
  • Backend Recommendations:
    • Phoenix (Elixir)36 for Postgres integration, pubsub, and presence (Erik Johansson).
  • Code Organization:
    • Zod schemas in monorepos for shared type safety (Franco Rodríguez Roura).
    • Linaria37 for styled components without runtime costs (Gregory Zoeller).
    • Hygen38 for efficient code generation (Justin Farrell).

Architects Are the New Developers

linkedin.com
Author: Robert Ranson

  • Architects empowered by AI: Architects can design systems and use AI to generate production-ready code from high-level specifications.
  • Agentic Engineering: Focus on system behavior and functionality over coding details, enabled by AI tools interpreting and executing architect specifications.
  • Key benefits:
    • Efficiency: Shorter development cycles.
    • Cost savings: Automating programming reallocates resources.
    • Higher quality: Consistent standards, comprehensive testing, and improved performance.
  • Challenges: Security concerns due to predictable AI-generated frameworks; human intervention needed for unique, secure designs.
  • Proficiency shift: Architects must master AI tools to remain competitive, emphasizing design intention over technical details.
  • Vision: Faster feature deployment and creative problem-solving, reshaping the role of architects as drivers of innovation.

Additional Notes:

  • Analogies to construction management highlight the architect’s role as pivotal for success when AI acts as the builder.
  • Early AI-generated code fits prototyping but requires skilled developers for production readiness.
  • Community engagement shows excitement, concerns over security, and eagerness for more functional AI agents.

Referenced Concepts and Tools3940:

What Separates $300K PMs from $700K PMs

linkedin.com
Author: Alex Rechevskiy

  • Core Insight: Higher-paid PMs excel at operating at scale, not necessarily in experience or technical skills.
  • Comparison Between $300K and $700K PMs:
    1. Problem Framing: $300K focuses on general goals (e.g., increase engagement); $700K focuses on targeted, scalable metrics (e.g., DAU/MAU in emerging markets).
    2. Solution Design: $300K suggests features; $700K crafts multi-quarter platform strategies.
    3. Impact Measurement: $300K looks at usage spikes; $700K ties improvements to financial outcomes.
    4. Stakeholder Management: $300K gains manager buy-in; $700K aligns VPs across multiple teams.
    5. Vision Setting: $300K plans next quarter; $700K envisions industry transformation over years.
  • Takeaway: Skills and their communication can be learned to bridge the gap.4142

Comments:

  • Concerns about exaggerated salaries (skepticism about $700K roles).
  • Strategic thinking over project-focused execution emphasized.
  • Practical barriers like company or customer readiness highlighted.
  • Some agreement on the importance of outcome-driven and strategic mindsets.
  • Speculation that scaling skills may correlate with experience.

UniteAI: Your AI Stack in Your Editor

github.com
Author: Josh Freckleton

  • Overview:
    • UniteAI integrates AI features like voice-to-text, local LLMs, GPT, and more into text editors via LSP.
    • Works with VSCode, Emacs, and any LSP-compatible editor, prioritizing editor-agnostic, Python-based configurations.
  • Core Features:
    • Document Chat (RAG): Semantic search over YouTube transcripts, Arxiv papers, PDFs, Git repos, and HTML via YAML queries.
    • Transcription: Real-time voice-to-text with portaudio dependency.
    • Local LLM: Enables large model inference via llama-cpp-python, supporting Falcon and similar models.
    • OpenAI Integration: GPT and ChatGPT queries directly in editor.
  • Installation:
    • Install dependencies with pip install uniteai[all] or granular installs for specific features (e.g., transcription, local LLM).
    • Editor configuration supports VSCode (uniteai.vsix extension), Emacs (lsp-mode), and other editors.
    • Long-lived LLM server ensures models persist across sessions for performance.
  • Customization:
    • Configurable keybindings for semantic search, text generation, voice-to-text, and streaming management.
    • .uniteai.yml file for module configuration.
  • Notable Dependencies:
    • Flash Attention: Optimized model speedup via pip install flash-attn.
    • Portaudio: Required for transcription features.

Tools and Links Captured43444546

Joy, Inc.: How We Built a Workplace People Love

Amazon

  • Cultural Transformation at Menlo Innovations:

    • CEO Richard Sheridan prioritized “joy” in workplace culture, eliminating fear and ambiguity.
    • Open, collaborative spaces and “pair programming” foster teamwork and reduce silos.

  • Innovative Practices:

    • Daily stand-up meetings with a Viking helmet to ensure transparency.
    • Radical hiring focused on cultural fit through teamwork tasks.
    • Preference for face-to-face communication over digital messages.

  • Driving Innovation and Learning:

    • Safe experimentation encouraged with the mantra “Let’s run the experiment.”
    • Weekly client feedback sessions for iterative improvement.
    • Visual project management tools like sticky-note tracking.

  • Elimination of Fear:

    • Mistakes embraced to foster growth.
    • Non-punitive support for realistic estimations builds trust.

  • Key Outcomes:

    • Sustainable growth, awards, and recognition as a cultural model.

CORAG: A Cost-Constrained Retrieval Optimization System for RAG

linkedin.com
Author: Sachin Kumar

Key Points47

  • Novel Contributions:

    • First RAG framework to consider chunk combination order in the retrieval task.
    • Integrates budget constraints into chunk utility optimization, addressing non-monotonicity and correlations.
    • Contrastive learning-based dynamic agent adjusts MCTS configurations per query, optimizing reranker models.

  • Workflow:

    • Embedding-based retrieval feeds a configuration agent.
    • Monte Carlo Tree Search (MCTS) determines the optimal chunk order and combination.
    • Final chunk combination is used to create LLM prompts.

  • System Features:

    • Policy tree reframes chunk combination as a node search problem.
    • MCTS optimizes search, balancing utility with cost constraints.
    • Contrastive learning improves query-specific configuration via joint loss function.

  • Results:

    • 25% improvement over baselines (NaiveRAG, RAPTOR).
    • Balances efficiency and relevance in retrieval tasks.
    • Exceptional scalability for datasets like WikiPassageQA and MARCO.

The AI Agent Race is On

linkedin.com
Author: Ivan Landabaso

The AI Agent race is on. Ivan Landabaso shares 10 key resources to surf the AI agent wave:

AI Market Supercycle and the Shift to “Services-as-Software”

linkedin.com
Author: Scott Brinker

  • Scott Brinker:

    • “Services-as-Software” (SaaS 2.0) as the next major trend with AI driving efficiency, innovation, and reinvention.
    • Legacy “Software-as-a-Service” will grow as an orchestration layer in an AI-driven future.
    • Massive opportunities for startups in services leveraging AI.

  • Comments:

    • Victor Harrison: Legacy tools like Salesforce and Adobe retain dominance due to integration and AI adoption; value perception drives adoption, not naming conventions.
    • Nataly Kelly: Evolution of pricing models as AI commoditizes services; example from translation shows how AI-human workflows blend cost structures.
    • Aditya Vempaty: Tailored, usage-based pricing aligns services with concierge-like AI agents.
    • Alex de Bold: Incumbents control critical data, creating competitive “data moats.”

  • Risks and Debates:

    • Can “New Guard” tools overcome incumbents’ data advantage and switching costs?
    • Emerging concepts: AI “Agent Meshes” and distributed workflows in service automation.
    • Pricing complexities as AI’s role in service provision becomes less distinguishable.

Resources and Tools4849

The Biggest Immigration Secret: Cap-Exempt Employment

linkedin.com
Author: Deedy Das

  • Immigration Strategy: Secure part-time employment (~5 hours/week) with a cap-exempt employer, such as a university, and concurrently file for a regular H-1B visa with a non-cap-exempt employer.
  • Legal Expertise: Work with an immigration lawyer or consult Danielle Goldman from The Build Fellowship for guidance.
  • Additional Resources:

Comments:

  • Vincent Granville: Genuine marriage to a U.S. citizen as an alternative route to a green card.
  • Uttam Bhetuwal: Concerns over high costs (~$50K/year); suggests Day 1 CPT as a cheaper option.
  • Suyash Kumar: Notes political sensitivity of sharing such information publicly.

CoRNStack: A High-Quality Contrastive Text-Code Dataset

linkedin.com Project Page
Author: Revanth Gangi Reddy
Date: November 2023

  • CoRNStack Dataset5253:

    • 21M <query, positive, negative> triples for training code embedding models.
    • Uses consistency filtering to remove noisy positives.
    • Includes hard negatives for improved contrastive training.

  • CodeRankEmbed:

    • 137M parameters.
    • Outperforms larger models like Codesage-Large and Voyage-Code-002.
    • Achieves state-of-the-art performance on code retrieval benchmarks.

  • CodeRankLLM:

    • Listwise code reranker trained on CoRNStack.
    • Improves ranking performance over other text rerankers.

  • Combined Model Results:

    • CodeRankEmbed + CodeRankLLM outperforms Agentless on SWE-Bench-Lite.
    • Excels in function localization for GitHub issues.

  • Resources:

Collaborators:

  • Siebel School of Computing and Data Science (Tarun Suresh, Yifei Xu, Heng Ji).
  • Nomic AI (Zach Nussbaum, Andriy Mulyar, Brandon Duderstadt).

Real OG Developer Practices

linkedin.com
Author: Alan Mellor

  • Core Practices for OG Developers:

    • Write clean, clear code first time; avoid overthinking.
    • Develop in tiny, shippable steps for incremental delivery.
    • Treat code as storytelling; prioritize clarity and simplicity.
    • Refactor constantly to explore design alternatives and maintain adequacy.
    • Ensure software works for what users care about; verify thoroughly.
    • Avoid team delays by writing self-explanatory code.
    • Shift left: prioritize earlier testing and planning in the lifecycle.
    • Capture everything (config, documentation, etc.) as code.

  • Key Comments:

    • Richard Audet: Queries the meaning of “shift left.”
    • Tommy Hinrichs: Asks about “capture everything as code” in early development days.
    • Vladan Ulardzic: Adds, “Learn the domain for faster and easier development.”
    • Sebastian Larsson: Suggests, “Communicate clearly in code and text.”
    • Promise Akeni: Highlights the wisdom of “Verify, don’t guess.”

Comprehensive Job Boards for All and Design-Specific Roles

linkedin.com
Author: Veena Mahesh

Job Boards for All Roles:

  1. Built In – Jobs in major US cities.
  2. Jobright.ai – AI-enhanced job search platform.
  3. Scale.jobs – Location-based tech roles; Techstars’24-backed.
  4. Indeed – Popular LinkedIn alternative.
  5. Elpha – Startup-focused job board.
  6. Remotive – Fully remote global opportunities.
  7. Working Nomads – Remote jobs worldwide.
  8. Authentic Jobs – Developer-focused listings.
  9. RemoteOK – Remote-first job opportunities.
  10. TechChange – Roles with global impact.

Job Boards Exclusive for Design:

  1. Early Stage Design Jobs – Internships and entry-level design roles.
  2. Open Doors – Diverse design and occasional product management roles.
  3. UXR Hunt – User experience research opportunities.
  4. Design Gigs for Good – Volunteer-run platform for meaningful design roles.
  5. Authentic Jobs – Great for designers as well.

References: 55 56 57 58 59 60 61 62 63

How to Succeed in MrBeast Production

drive.google.com
Author: Jimmy Donaldson (MrBeast)

  • Core Philosophy:

    • The ultimate goal: Make the best YouTube videos, not just high-quality or well-produced ones.
    • Stay nimble and adopt YouTube-first strategies over traditional media methods.
    • Results matter more than hours worked—outcomes drive success.

  • Key Metrics for Virality:

    • Click Through Rate (CTR): Driven by compelling thumbnails and titles.
    • Average View Duration (AVD): Retention graphs guide improvements, especially the crucial first minute.
    • Average View Percentage (AVP): Indicates overall viewer engagement.

  • Production Essentials:

    • Always know the title and thumbnail to align expectations with content.
    • Treat critical components (e.g., titles, set pieces) as irreplaceable—double-check, back up, and obsess over them.
    • Daily check-ins with bottlenecks; accountability and proactive tracking are non-negotiable.

  • Creative Insights:

    • Videos must excite MrBeast—innovation, simplicity, and “wow factor” are critical.
    • Use formats like stair-stepping (e.g., escalating stakes) or payoff-based structures for retention.
    • Continuously innovate; never rely too long on the same format.

  • Team Dynamics:

    • Cultivate an information diet to stay culturally relevant and inspired.
    • Communicate effectively—use the highest form of communication necessary for clarity.
    • Own mistakes and prioritize learning over saving face.

  • Operational Guidelines:

    • Never take “no” at face value; explore every avenue to achieve outcomes.
    • Work on multiple videos daily to avoid falling behind.
    • Creativity saves money—find imaginative solutions instead of relying on budget increases.

  • Career Growth:

    • Opportunities for leadership abound; request reviews and improve based on feedback.
    • Success at MrBeast Productions comes with rewards tied to responsibility and results.
    • Treat the company as a long-term career destination with limitless potential.

  • Miscellaneous Tips:

    • Record everything during set scouting for team alignment.
    • Integrate brand deals seamlessly to maintain viewer retention.
    • Always push for better pacing, lighting, music, and storytelling.

How to Learn Rust in 2024: A Complete Beginner’s Guide

jetbrains.com
Author: Vitaly Bragilevsky

  • Core Concepts:

    • Ownership & Memory Management: Memory safety through ownership, borrowing, and lifetimes.
    • Concurrency: Fearless concurrency using ownership, immutability, and message passing.
    • Traits & Generics: Abstracting behavior for types.

  • Learning Resources:

    • Books:
    • Tutorials:
    • YouTube:
      • Let’s Get Rusty and Jon Gjengset for engaging and in-depth content.
    • Community:

The Elements of APIs: A Shortcut

johnholdun.com

  • API Principles:

    • Serve JSON over HTTP and adhere to strict RESTful practices.
    • Define all possible valid requests via OpenAPI specs, freely available to users.
    • Follow resource-based design with standard HTTP verbs (GET, POST, PATCH, DELETE).

  • Path and Response Design:

    • Path patterns: /resource_type, /resource_type/resource_id, /resource_type/resource_id/relationship_name.
    • Responses: JSON with data for success, error for failure, consistent pagination, and filtering support.

  • Implementation Best Practices:

    • Enforce OpenAPI specs for consistency and error handling (e.g., express-openapi-validator).
    • Validate input/output against specs to streamline functional testing.

  • Design Guidance:

    • Use predictable, flat JSON structures for resources (type, id, attributes, relationships).
    • Establish clear relationships between resources (to-one or to-many), avoiding optional or implicit relationships.

  • Versioning and Change Management:

    • Avoid breaking changes; only add fields or create new resources.
    • Provide clear documentation and avoid deprecating functional elements unless absolutely necessary.

  • Discoverability vs. Documentation:

    • Focus on ergonomic design rather than full HATEOAS; robust documentation is essential.
    • Keep the API predictable for easier integration and maintenance.

  • Key Advice:

    • “Many nouns, few verbs”: Limit interactions to core HTTP methods.
    • Design data deliberately with input from stakeholders, ensuring resources align with user journeys.

Tools and References Captured64:

SaaSavant SaaS Boilerplate Overview

saasavant-saas-boilerplate.hookerhillstudios.com
Author: jrh89

  • Pre-built functionality:
    • Stripe Payments, SendGrid Emails, Firebase Auth, and DB.
    • Admin announcements, Toast Notifications, Newsletter, Landing Page.
    • Support Tickets, User Management, Account Management, Lead Capture.
  • Ease of Use: Ships as a functional SaaS; just add environment variables and unique features.
  • No Subscription: Lifetime license for $29.99 (early bird pricing).

Comments

  • Cronitor Launch Story (encoderer):
    • Minimal setup (Django, $20 bootstrap, Twilio, Mailchimp, Stripe Checkout).
    • Focused on lean development to avoid excess.
  • Cookiecutter Django:
  • OpenSaaS Comparison (jrh89 & hot_town):
    • SaaSavant: Easier setup with more out-of-the-box functionality.
    • OpenSaaS: Requires configuration but offers thorough documentation and tools like wasp db start for local Postgres (Docs, Discord)

OpenRouter Quick Start Guide

openrouter.ai

  • OpenAI-Compatible API: Supports 290+ models and providers; integrates with OpenAI SDK and third-party SDKs.
  • Standardized API: Seamless switching between models/providers without code changes; users can choose/pay for their own models.
  • Routing Options:
    • Load balancing across stable providers; prioritizes cost-effective ones.
    • Custom routing and fallback settings to control provider preferences.
    • Quantization levels filter models by computational efficiency (e.g., int4, fp8).
  • Data Privacy: Flexible data policies; disable providers that log prompts or store data.
  • Billing and Availability: Consolidated billing, better rate limits, and automatic routing for uptime.

Structlog: Structured Logging for Python

structlog.org

  • Structured Logging: Simplifies log output by combining context and event details into dictionaries, allowing flexibility and easy manipulation.
  • Processor Chains: Modular processing of logs through callable functions; supports dynamic transformations like adding timestamps or filtering events.
  • Context Management: Use bind/unbind methods to add or remove contextual key-value pairs across log entries; supports thread-local storage for global data.
  • Customizable Renderers: Output logs in formats like JSON, or use processors like ConsoleRenderer for human-readable logs with optional colorized output.
  • Integration-Friendly: Compatible with Python’s standard logging, Twisted, and async loggers. Easy to integrate with existing systems using factories and wrappers.

Getting Started with Testcontainers for Python

testcontainers.com

Key Takeaways

  • Purpose: Testcontainers simplifies testing with real containers, mimicking production environments (e.g., databases, message brokers).
  • Setup:
    • Use psycopg, pytest, and testcontainers[postgres].
    • Create a virtual environment and install dependencies.
    • Generate a requirements.txt for reproducibility.
  • Database Helper:
    • Environment variables configure connection details dynamically.
    • Example function: get_connection() for PostgreSQL access.
  • Business Logic:
    • Implement CRUD operations and schema creation for a customers table using psycopg.
    • Example methods: create_customer(), get_all_customers(), and delete_all_customers().
  • Test Design:
    • Use pytest fixtures for setup/teardown.
    • Leverage Testcontainers to create a PostgreSQL container.
    • Tests run with clean state using setup_data() to clear records.
  • Tests:
    • Validate functions like fetching all customers (get_all_customers) and querying by email (get_customer_by_email).
  • Execution:
    • Run tests using pytest. Real PostgreSQL containers ensure reliable results.

—

Building a Better and Scalable System for Data Migrations

yorickpeterse.com
Author: Yorick Peterse
Date: October 24, 2024

  • Core Problems with Current Migration Systems:

    • Timelessness: Existing systems assume migrations always work, but app logic changes can break them.
    • Scalability: Most systems lack tools to handle large datasets or distributed environments.
    • Testing Gaps: Limited primitives for testing migration correctness.
    • Code Duplication: Isolated migrations require duplicating logic, increasing maintenance effort.

  • Proposed Improvements:

    • Function-Based Migrations: Use programming languages for migrations with dedicated up and down functions.
    • VCS-Revision-Specific Runs: Link migrations to specific VCS states for reproducibility and easier rollbacks.
    • Split Migrations: Pre-deployment for backward-compatible changes; post-deployment for breaking changes.
    • Large Data Handling: Distributed processing, background job support, and non-blocking deployments for large datasets.
    • Testing Support: Introduce primitives to validate migrations through bi-directional tests (e.g., up and down flows).

  • Key Features for Scalability:

    • Fork-Join Model: Distribute workloads across hosts for parallel processing.
    • Background Jobs: Enable stable application logic during large-scale migrations.
    • Progress Monitoring: Track and report migration progress through logs.

  • Conclusion:

    • Introducing revision-specific migrations can enhance timelessness.
    • Emphasizes building scalable, automated systems with robust testing.
    • Highlights potential for further exploration in frameworks like Inko.

Running a Bootstrapped Company for $524/month

linkedin.com
Author: Iuliia Shnai (original post), reposted by Louis Gleeson

  • $524/month Tech Stack:

    • Semrush: $139 (Marketing/Keywords)
    • Resend: $100 (Emails)
    • Vercel: $100 (Hosting)
    • Cursor AI: $40 (AI code editor)
    • OpenAI: $40 (AI chat)
    • PlausibleHQ: $30 (Analytics)
    • Typefully: $25 (Scheduling)
    • AnthropicAI: $20 (AI chat)
    • Tinybirdco: $20 (Analytics)
    • AWS Cloud: $10 (Storage)

  • Key Insights:

    • AI and SaaS advancements enable ultra-lean operations for small teams.
    • Predicts emergence of one-person billion-dollar companies within five years.

  • Free AI Tools:

    • Basedlabs
    • Fireflies
    • ChatGPT
    • Perplexity

Original post credited to Iuliia Shnai.

Resources656667368697071727374757677:

ColPali: Enhancing RAG for PDF Documents with Multimodal Retrieval

linkedin.com
Author: Sarthak Rastogi

  • Overview:

    • ColPali improves Retrieval-Augmented Generation (RAG) on PDFs, achieving 65.3% better scores on the DocQ benchmark.
    • Available on Hugging Face: ColPali Model.

  • Key Features:

    1. Vision-Language Models (VLMs): Adapts VLMs for multi-modal fine-tuning to align text and image embeddings.
    2. Multi-Vector Representations: Generates vector embeddings for both text and images.
    3. Late Interaction (LI): Computes query-document similarity with a late interaction operator.
    4. Contrastive Loss Training: Uses query-page pairs for precise retrieval.
    5. OCR-Free Processing: Encodes pages directly from image representations, enhancing speed and bypassing preprocessing.

  • Advantages:

    • Combines textual and visual elements for improved accuracy and efficiency.
    • Handles visual information (e.g., tables, images) natively without OCR.
    • Suitable for scenarios requiring visual proof alongside LLM-generated answers.

  • Community Contributions:

  • Open Questions:

    • Impact on latency with large documents.
    • Comparison with other tools like Llamaparse.
    • Compute requirements for scaling.

Resources807879:

Footnotes

  1. Color Blindness Statistics – Red-green color blindness prevalence data. ↩

  2. Original article: Cursor Review ↩

  3. Cursor – AI-powered code editor. ↩ ↩2

  4. Supermaven – For contextually logical completions. ↩

  5. Lex Fridman Podcast on Cursor – Discussion on Cursor’s development. ↩

  6. JetBrains AI Plugin – Alternative IDE integration. ↩

  7. Canvas Tool – Web-based alternative. ↩

  8. Anthropic Claude – AI model used in Cursor. ↩

  9. Continue – VS Code AI extension. ↩

  10. Load vector embeddings up to 67x faster with pgvector and Amazon Aurora ↩ ↩2

  11. Nested hashes for variable-length embeddings ↩

  12. Space complexity of vector search indexes in libsql ↩

  13. Superlinked guide on vector indexes ↩

  14. LitServe GitHub repository GitHub ↩

  15. ZenML – MLOps framework for pipelines and integrations. ↩

  16. Hugging Face TGI – Inference optimization tool. ↩

  17. Intel LLMOps – Strategies for scalable LLM production. ↩

  18. LinkedIn 101 Guide ↩

  19. The Ultimate Guide to H1B Application ↩

  20. How to Land a Top Internship ↩

  21. Growth Unhinged – Kyle Poyar’s newsletter covering SaaS trends and insights. ↩

  22. Next.js – Framework for server-rendered React applications. ↩

  23. Vite – Build tool for fast application scaffolding. ↩

  24. Tailwind CSS – Utility-first CSS framework. ↩

  25. Zod – Schema validation library. ↩

  26. React Query – State management for server-state. ↩

  27. Zustand – Lightweight state management. ↩

  28. Shadcn UI – Minimalistic component library. ↩

  29. OpenAPI Zod Client – Code generation for API clients. ↩

  30. Remix – Framework for SSR and SPAs. ↩

  31. DaisyUI – Tailwind-based UI library. ↩

  32. Mantine – Comprehensive UI library. ↩

  33. TanStack Router – Type-safe router. ↩

  34. React Hook Form – Library for form handling in React. ↩

  35. tRPC – Type-safe APIs with Zod integration. ↩

  36. Phoenix Framework – Web framework for Elixir. ↩

  37. Linaria – Zero-runtime CSS-in-JS. ↩

  38. Hygen – Tool for scaffolding and code generation. ↩

  39. Agentic Engineering: AI-driven approach prioritizing system design over programming. ↩

  40. “The RUV Bounce”: Framework mentioned in the post as an enabler of these AI-driven architectural shifts. ↩

  41. Original LinkedIn post: What Separates $300K PMs from $700K PMs ↩

  42. Alex Rechevskiy Coaching Program – Group coaching for ambitious PMs. ↩

  43. UniteAI GitHub Repository – LSP-based AI stack for text editors. ↩

  44. Flash Attention – Speed optimization for LLMs. ↩

  45. llama-cpp-python – Backend for local LLM inference. ↩

  46. PortAudio – Dependency for voice transcription. ↩

  47. CORAG: A Cost-Constrained Retrieval Optimization System ↩

  48. Battery Ventures’ 2024 OpenCloud Report – Comprehensive analysis of AI market trends. ↩

  49. AI Agent Ops Group – Discussions on AI agent workflows and infrastructure. ↩

  50. Global Talent Webinar by Build Fellowship ↩

  51. OfferPilot Visa-Sponsored Jobs ↩

  52. CoRNStack Dataset – Official project page. ↩

  53. Evaluation Code – GitHub repository for CoRNStack evaluation. ↩

  54. Sentence Transformers – Framework for embedding sentences and code. ↩

  55. Built In – Jobs in major US cities. ↩

  56. Jobright.ai – AI-enhanced job search platform. ↩

  57. Scale.jobs – Location-based tech roles. ↩

  58. Authentic Jobs – Developer and design roles. ↩

  59. Remotive – Fully remote global opportunities. ↩

  60. Working Nomads – Remote job opportunities. ↩

  61. TechChange – Roles with global impact. ↩

  62. UXR Hunt – User experience research jobs. ↩

  63. Design Gigs for Good – Volunteer-driven design platform. ↩

  64. Express OpenAPI Validator – Middleware for validating OpenAPI-compliant requests and responses. ↩

  65. Semrush – Marketing and keyword research platform. ↩

  66. Resend – Email management and delivery platform. ↩

  67. Vercel – Hosting and deployment for web apps. ↩

  68. OpenAI – AI chat and GPT-based services. ↩

  69. Plausible Analytics – Privacy-focused web analytics. ↩

  70. Typefully – Content scheduling and management tool. ↩

  71. Anthropic AI – AI chat and research tools. ↩

  72. Tinybird – Real-time data analytics platform. ↩

  73. AWS Cloud – Cloud storage and services. ↩

  74. Basedlabs – AI tools and utilities. ↩

  75. Fireflies – AI meeting transcription and insights. ↩

  76. ChatGPT – Conversational AI by OpenAI. ↩

  77. Perplexity – AI-powered search and assistance. ↩

  78. Multi-Modal RAG Chatbot – Combines visual and textual elements in RAG. ↩ ↩2

  79. Vision Is All You Need – Applied AI RAG with ColPali retrieval. ↩ ↩2

  80. ColPali on Hugging Face – Multimodal RAG model for PDFs. ↩