DEV Community: CyprianTinasheAarons

The "Vibe Coding" Trap 🤖🔥

CyprianTinasheAarons — Wed, 11 Mar 2026 12:56:50 +0000

Why AI-Native Devs Still Need to Understand LLM Architecture

The Conversation I Keep Having 👀

"I'm vibe coding now — Claude / Cursor just does it all."

I hear this 3 times a week from developers in my network.

And honestly… I get it.

That dopamine hit of shipping features in 20 minutes is real.
You prompt → code appears → tests pass → deploy 🚀

Feels like magic.

But here's the thing most people aren’t talking about:

Vibe coding works… until it doesn't.

And when it breaks, you have absolutely no idea why.

3 Real Cases From Recent Interviews 🎤

1️⃣ Context Window Blindness

A developer built an agent with 50+ tool calls per request.

Testing?
Worked perfectly. ✅

Production?
50% failure rate. ❌

The problem

They didn’t realize:

Tool definitions count as tokens
Conversation history counts as tokens
System prompts count as tokens

That 128k context window disappears FAST when you are verbose.

💡 Result: prompts were getting silently truncated.

2️⃣ The Temperature Problem 🌡️

Developer complaint:

"My outputs are inconsistent."

We looked at the config.

temperature = 0.7

For a deterministic task.

Temperature basically controls randomness.

Think of it like this:

Temperature	Behavior
0.0	deterministic / consistent
0.3	slightly flexible
0.7	creative
1.0	chaos mode

They wanted structured outputs.

But they configured the model for creative writing 😂

3️⃣ Hallucination Blindspot 🧠💥

Agent kept making confident but wrong API calls.

It cost the team 6 hours of debugging.

The root issue?

They assumed the LLM knew facts.

It doesn't.

LLMs are basically:

Next-token prediction engines.

Not databases.
Not truth engines.

Without a validation layer, the model will happily invent things.

What Actually Matters 🧠

You don't need to understand transformer math.

But if you're building AI products, you must understand these basics:

🧾 Context Windows

You are paying for every token.

Design your systems around:

prompt compression
summarization
retrieval patterns
chunking

🌡️ Temperature & Top-P

Know when you want:

determinism (automation, APIs, agents)
creativity (content, ideation)

Wrong setting = unstable systems.

🔤 Tokenization Artifacts

Those weird bugs like:

off-by-one errors
truncated prompts
unexpected formatting

Often come from tokenization quirks.

🧭 System Prompt Weight

Your system instructions are competing with training data.

Position matters.
Structure matters.

Sometimes moving instructions earlier fixes everything.

📦 Structured Output

Use constraints when possible:

JSON mode
function calling
response_format
schema validation

Never trust free-form text in production systems.

The Real Bottom Line ⚡

Vibe coding is incredible.

It’s a productivity multiplier.

But it is not a skill replacement.

The devs who will dominate the next 5 years will:

Vibe code 80% of the boilerplate
Engineer the 20% that actually matters

That 20% is where real systems are built.

Your Turn 👇

What’s the biggest vibe-coding failure you've experienced?

Context limits?
Hallucinations?
Agent chaos?

Drop it below 👇

Let's learn from the war stories 😄

Google's A2A Protocol: The HTTP for AI Agents Nobody Asked For, But Everyone Needs

CyprianTinasheAarons — Tue, 10 Mar 2026 12:51:24 +0000

The Fragmentation Problem

We have a problem. In 2026, we're drowning in AI agents. Every SaaS product has one. Your CRM has an agent. Your email has an agent. Your calendar, your IDE, your analytics dashboard—all agents, all siloed. They don't talk to each other. They don't share context. They don't collaborate.

The result? Digital busywork. You copy prompts between tools. You manually hand off tasks. You become the integration layer, stitching together workflows that should be autonomous.

This wasn't the promise. The promise was agents working together—specialized AI teammates collaborating on complex, long-running tasks without human micromanagement.

Enter A2A: Simple, Open, Universal

Google's Agent2Agent (A2A) protocol, announced in April 2025 and now on v0.3.0, aims to solve this problem.

It does what HTTP did for the web: provide a common language any AI agent can speak, regardless of who built it or what framework it runs on.

The protocol is intentionally minimal:

No custom binary formats
No proprietary transports
Simple HTTP + JSON communication

If your agent can make HTTP requests and parse JSON, it can speak A2A.

Version 0.3.0 added optional gRPC support for high-performance deployments, while HTTP remains the baseline.

How It Works: Three Core Primitives

A2A is built around three simple concepts.

Agent Cards

Public metadata describing:

What an agent does
What inputs it needs
How to authenticate

Think of it as robots.txt for AI capabilities.

Task Management

Standardized messages for delegating work:

Task requests
Status updates
Streaming responses
Task completion

Agents can hand off work and track progress asynchronously.

Context Sharing

A mechanism for sharing relevant information without exposing sensitive internal data.

The receiving agent gets only what it needs to complete the task, not a full data dump.

The Linux Foundation Move

In June 2025, the Linux Foundation launched the Agent2Agent Protocol Project.

This is important.

Corporate-led standards often fade when the originating company loses interest.

Linux Foundation governance provides:

Neutral stewardship
Open collaboration
Long-term viability

More than 50 technology partners have joined, including:

Atlassian
Box
MongoDB
Salesforce
SAP
ServiceNow

A2A is no longer a Google experiment. It is evolving into an industry standard.

Why Now?

2025 was the year of single agents.

2026 is the year of multi-agent systems.

Industry momentum is already visible:

Anthropic’s multi-agent research platforms
Salesforce’s Agentforce orchestration
OpenAI’s Swarm framework

The transition from demo prototypes to production systems requires interoperability.

Enterprises will not deploy dozens of disconnected agent frameworks.

They need a protocol layer.

A2A is positioning itself to become that layer.

Real-World Applications

Supply Chain Coordination

A logistics agent communicates with:

a warehouse agent
a shipping agent
a customs agent

Each specializes in a domain. Each delegates subtasks.

Routine coordination happens without human intervention.

Healthcare Workflows

A triage agent:

Routes cases to diagnostic agents
Diagnostic agents consult specialist agents
Specialist agents coordinate with scheduling agents

Complex patient journeys become coordinated systems instead of fragmented workflows.

The Implications for Developers

Developers building agents today face a strategic decision:

Build another silo, or build on an interoperable standard.

With A2A:

Your Slack bot can delegate tasks to a code review agent
Your support agent can escalate to a specialist agent
Your product analytics agent can collaborate with a marketing agent

Agents become services in a network, not isolated applications.

The network effects grow as more agents adopt the protocol.

Challenges and Open Questions

A2A is promising but not complete.

Open problems remain:

Identity verification between agents
Trust and reputation systems
Preventing malicious agents
Auditing multi-agent transactions

Google is supporting adoption with:

A2A Interactions API
Agent Development Kit (ADK)

However, standards require time and ecosystem growth.

HTTP itself took years before universal adoption.

A2A will need critical mass to succeed.

The Bottom Line

We are reaching an inflection point.

Single AI agents are becoming table stakes.

The new competitive advantage lies in orchestration—how effectively agents collaborate.

A2A represents a strong bet that interoperability will win over walled gardens.

Given its growing partner ecosystem and Linux Foundation backing, it is a development worth watching.

If you are building agentic systems today, you should strongly consider building on A2A—or have a compelling reason not to.

The multi-agent future is already emerging.

A2A may become the connective tissue that makes it possible.

I Replaced My Entire Productivity Stack with an AI Agent Running 24/7

CyprianTinasheAarons — Sat, 07 Mar 2026 15:36:13 +0000

I Was Drowning in Tools

Notion. Trello. Google Calendar. Gmail. Telegram. Discord. LinkedIn. Dev.to.

Every morning I'd open 8 tabs, check 4 apps, copy-paste between 3 dashboards, and somehow still miss important emails. I was spending more time managing my systems than actually doing work.

Then I found OpenClaw — an open-source framework that lets you run a personal AI agent 24/7. Not a chatbot. Not a copilot that waits for you to ask. A persistent agent that proactively manages your life while you sleep.

Here's how I set mine up — and what a typical day looks like.

What is OpenClaw?

OpenClaw is a self-hosted AI assistant framework. Think Jarvis, but real. It runs as a Node.js service that connects to your messaging platforms (Discord, Telegram, WhatsApp, iMessage) and gives an AI agent access to tools: shell commands, browser automation, file management, APIs, cron jobs, and more.

The key difference from ChatGPT or Claude's web interface: it runs continuously. It has persistent memory. It can reach out to you — not just respond when prompted.

My Setup: The EPA (Execute Personal Assistant)

I named my agent Molty 🐒. It runs on a cloud instance with these integrations:

Discord — Primary communication channel
Telegram — Alerts only
Gmail/Calendar — Email monitoring + calendar pulls
Life OS Dashboard — Custom Next.js app with tRPC + Neon DB for tasks, jobs, finance, daily logs
GitHub — Authenticated for repo management

The Personality Layer

OpenClaw lets you define your agent's personality through SOUL.md. Mine is configured to be direct, no-fluff, and to actively push back on bad decisions. It knows my goals ($1M by end of 2026), my tendencies (impulsive under stress), and my priorities (money > everything else).

This isn't a gimmick — it means when I'm about to rage-quit a project at 1 AM, my agent literally asks: "Is this the move, or is this the mood?"

The Automation Stack

🎯 Job Hunting on Autopilot (4 batches/day)

I'm actively job hunting for senior remote roles. Instead of manually browsing job boards, I have 4 daily cron jobs that:

Search WeWorkRemotely, RemoteOK, Himalayas, Arc.dev, and others
Compile 25 opportunities per batch (100/day total)
Score each by match level against my skills
Post the full list to Discord #job-batches
Create top entries in my Life OS database

Schedule:

Time	Batch
07:00	Batch 1/4 (25 jobs)
11:00	Batch 2/4 (25 jobs)
15:00	Batch 3/4 (25 jobs)
19:00	Batch 4/4 (25 jobs)

That's 100 curated job opportunities landing in my Discord every single day without me lifting a finger.

💪 Motivation System (5 drops/day)

Context-aware motivation drops that reference my actual goals, current projects, and progress. Not generic Instagram quotes.

Time	Drop
09:00	Morning fire
12:00	Midday push
15:00	Afternoon grind
18:00	Evening reminder
22:30	Night reflection

📊 Daily Operations

06:00 — Morning briefing (weather, calendar, emails, priorities)
07:30 + 13:00 — Personal brand warm-up (trending topics for Twitter/LinkedIn)
21:00 — Evening wrap-up (what got done, metrics, tomorrow's plan)

🧠 Heartbeat System

Every hour, OpenClaw runs a "heartbeat" — a health check that scans all cron jobs for missed runs, recovers failures by spawning sub-agents, and reports status. This morning, 3 cron jobs failed due to a misconfigured delivery channel. The heartbeat caught it, I fixed the routing, and every job since has delivered successfully. Self-healing automation.

The Memory System

This is what makes OpenClaw feel alive:

MEMORY.md — Long-term curated knowledge (contacts, decisions, preferences, lessons)
memory/YYYY-MM-DD.md — Daily logs (raw events, conversations, actions)
SOUL.md — Identity and personality
USER.md — Context about you (goals, style, decision patterns)

Every session, the agent reads these files to rebuild context. Mine knows I make impulsive decisions under stress, so it's configured to challenge me before validating any major call.

Real Numbers After 1 Month

Metric	Before	After
Job opportunities reviewed/day	5–10 (manual)	100 (automated)
Time on job search/day	45–60 min	10 min (review only)
Missed emails	Several/week	Near zero
Calendar surprises	Often	Never
Daily motivation	Random YouTube	5 targeted drops
Brand content	When I remembered	Scheduled daily

How to Set It Up

Install: npx openclaw@latest
Configure SOUL.md (personality), USER.md (your context), AGENTS.md (rules)
Connect channels (Discord recommended for structured delivery)
Set up cron jobs for your automations
Build your memory — start with basic MEMORY.md, let it grow

Lessons Learned

Start with one automation, not ten. Morning briefing first. Once solid, layer on more.
Delivery channel matters. Discord with dedicated channels > routing everything through one chat.
Timeouts are real. 180s for complex tasks, 60s for simple ones.
The heartbeat is your safety net. Self-healing cron recovery is a game-changer.
Personality isn't fluff. A well-configured SOUL.md = assistant you actually want to talk to.

The Bottom Line

OpenClaw isn't magic. It's infrastructure that compounds — every automation you add frees up time to build the next one. The future isn't AI replacing developers. It's developers who use AI agents outrunning everyone else.

[Boost]

CyprianTinasheAarons — Sat, 03 Jan 2026 14:05:07 +0000

CyprianTinasheAarons

Jan 1

My 2026 Job hunting list

#programming #webdev #javascript #ai

Comments 5

5 min read

Great Resource guide on getting hired in 2026.

CyprianTinasheAarons — Thu, 01 Jan 2026 09:23:45 +0000

CyprianTinasheAarons

Jan 1

My 2026 Job hunting list

#programming #webdev #javascript #ai

Comments 5

5 min read

My 2026 Job hunting list

CyprianTinasheAarons — Thu, 01 Jan 2026 09:21:28 +0000

Happy New Year to everyone reading this.

Starting the year can be difficult and challenging, especially if you're unemployed or looking for a new place to call home as an employee. I know this firsthand—the last two years have been tough for me. I've been unemployed, navigating the uncertainty, the rejection emails, the waiting, and yes, sometimes just holding onto hope that something will break through.

But I'm still here, still searching, still trying. And I thought that maybe there's someone like me out there who's looking for some work. If that's you, I hope this helps.

What I'm Sharing With You

I've curated a list of some of the best and favorite job sites that I've worked with to secure work over the years. Some are part-time opportunities, some are full-time positions. Some are contract and freelance gigs. The variety is there because different seasons of life call for different types of work.

But beyond just job links, I've also added resources that have helped me—platforms for upskilling, guides for crafting killer cover letters, and tips for refining your resume. All the best wishes as you dive in.

💡 My Tips for Your Job Search Journey

1. 🔔 Set Up Your Accounts and Email Alerts

Do take a deep dive into these links. Set up accounts, maybe even email alerts, and make it a habit. Consistency matters more than perfection. Apply to the best places you like—3 to 5 jobs a day would be good when it comes to freelancing. Maybe put in more effort in contract work since those might require detailed proposals and more preparation.

2. 📚 Upskill While You Search

I've added a roadmap platform where you can find different roadmaps to upskill. My tip and advice would be for you to use Google's NotebookLM to sort of curate different learning materials—quizzes, videos, explainers, podcasts, and mind maps. It just depends on how you learn best. So you can work with that and tailor your learning experience.

3. ✉️ Master the Cover Letter

Another big thing is for you to learn how to create good cover letters. For every job interview—and even when you're applying for platforms like Upwork—it is advised to put in a good, thoughtful cover letter. I've found one of the best explainers from Reddit in terms of creating a killer cover letter, and I'm sharing it below.

4. 📄 Refine Your Resume

I've also added another Reddit article that discusses the benefits and drawbacks of different resume approaches, as well as practical tips on refining and enhancing your resume. These insights come from someone who's reviewed over 1,000 resumes, so the advice is battle-tested.

💼 The Job Boards & Platforms

Here's a comprehensive list of job sites across different categories. I've organized them so you can focus on the ones that match your situation—whether you're looking for remote work, freelance gigs, or international opportunities.

🌍 Global & Remote-First Job Boards

💰 Freelance & Contract Platforms

💻 Tech-Focused & Developer Job Boards

🌏 Relocation, Japan, Asia & International Jobs

🏢 Corporate & High-End Tech Careers

🌍 Middle East, Gulf & Global Aggregators

🚀 Startup-Friendly Platforms

📚 Learning & Career Resources

Here are the additional resources I've gathered for you:

🎯 Interview Prep & Technical Skills

10 Best Tech Programming Interview Prep Resources in 2025 - Comprehensive guide for technical interview preparation

📈 Skill Development Roadmaps

Roadmap.sh - Different roadmaps for various career tracks to help you upskill strategically
Google NotebookLM - AI-powered tool to curate learning materials like quizzes, videos, explainers, podcasts, and mind maps

💬 Post-Interview Communication

Communication After the Interview | InterviewGuide.dev - Learn how to follow up professionally after interviews

📝 Resume Writing

I've Reviewed 1000+ Resumes - Here Are My Tips (Reddit) - Battle-tested advice on what makes a resume stand out

✍️ Cover Letter Mastery

My Guide to Writing a Killer Cover Letter (Reddit) - Detailed guide on crafting compelling cover letters that get noticed

📰 Staying Updated

daily.dev — Stay informed with current tech trends and news прямо from your browser dashboard. Great for keeping up with what's happening in the developer ecosystem daily.

💪 A Final Word

Sometimes we just have this hope, you know? Hope that the next application will be the one. Hope that someone will see our value. Hope that things will get better.

Hold onto that hope. But also take action alongside it. Use these resources. Apply consistently. Keep learning. Keep growing.

The job market is tough right now—I'm not going to sugarcoat that. But you're tougher. You're still here, still trying, still pushing forward.

And that counts for something.

All the best wishes for 2026. You've got this.

What resources have helped you in your job search? Feel free to share in the comments below.

Umoja — One Africa. One App.

CyprianTinasheAarons — Tue, 11 Nov 2025 14:50:27 +0000

Hi, my name is Cyprian Aarons, and I’m the founder of Umoja.

In Africa, most small businesses sell through WhatsApp, but they still manage payments, delivery, and accounting separately. It’s slow and messy.

Umoja fixes that.
We bring everything into one place — chat, POS, payroll, delivery, and accounting — all connected to WhatsApp.

A customer can message a shop on WhatsApp, place an order, pay, and get delivery — all through Umoja.

We’re starting in Zimbabwe, where over a million vendors already sell informally, and expanding across Africa’s 500 million WhatsApp users.

Our business model is simple: we take a small 1–3% fee on each transaction and offer premium plans for growing businesses.

We’ve built the platform and are raising funds to scale.

Umoja — One Africa. One App.

My 2025 AI Engineer Roadmap List

CyprianTinasheAarons — Mon, 16 Dec 2024 06:02:27 +0000

Developer Tools & Platforms

Frameworks / Infrastructure / Backends

Supabase: An open-source Firebase alternative offering Postgres, authentication, and storage solutions. 🚀
LangChain: A powerful framework for building applications powered by LLMs, enabling seamless integration of prompts, chains, and agents. 🧠
LlamaIndex: A tool for connecting LLMs with your own data, streamlining retrieval-augmented generation workflows. 📖
Streamlit: Quickly build interactive web apps for machine learning and data science projects. 💻
FastAPI: A modern Python framework for building APIs with automatic interactive documentation. ⚡
Django: A high-level Python web framework that encourages rapid development and clean, pragmatic design. 🌐
Flask: A lightweight WSGI web application framework in Python. 🧩

Roadmaps

Learning and Growth Paths

AI Engineer Roadmap: A step-by-step guide to mastering AI engineering in 2024. 🎓
Prompt Engineering Roadmap: Learn the art of crafting effective prompts for large language models. ✍️
Data Analyst Roadmap: A structured path to excelling as a data analyst. 📊
AI and Data Scientist Roadmap: A holistic view of the skills and technologies needed for AI and data science careers. 🧪
System Design and Software Architecture: Essential guides for designing scalable, efficient, and maintainable systems. 🏗️
Python Developer Roadmap: Learn to become a modern Python developer. 🐍

Tools

CopilotKit: Build advanced AI-powered apps quickly and effectively. 🤖
Langflow: Simplify the process of designing LangChain-based applications. 🎨
Zapier and n8n: Automation tools to integrate workflows across applications. 🔄
OpenRouter: A centralized platform to experiment with and deploy various LLMs. 🛠️
Elastic RAG Agent: Combining Elasticsearch and LangChain for retrieval-augmented generation tasks. 📂
Jupyter Notebooks: Interactive computing notebooks for data analysis and visualization. 📓
VS Code: A popular code editor with extensive AI and Python extensions. ✏️

Open-Source Projects

Noteworthy GitHub Repositories

LangChain RAG Template: A starter template for building Retrieval-Augmented Generation workflows. 🔍
Agent Tutorials: Hands-on tutorials for implementing various agents. 🤝
Next.js AI Starter: A Next.js template for SaaS apps integrating AI features. 💡
Voice Chat PDF: A tool to converse with PDFs using voice commands. 🗣️
Everything AI: A comprehensive collection of AI tools, libraries, and resources. 🛠️
Python Algorithms: A treasure trove of algorithmic implementations in Python. 🧮
OpenAI Assistants: Get started with deploying OpenAI-powered assistants. 🤖
EDA GPT: Explore AI-driven exploratory data analysis. 🔎

Learning Resources

Books

Courses

DeepLearning.AI: A wide variety of courses to master deep learning and related fields. 🧠
Coursera: Explore certifications, degrees, and free courses across AI, data science, and more. 🎓
LangChain Tutorials: Dedicated tutorials for leveraging LangChain effectively. 📘

Blogs

"How I Built the World’s Best AI Scheduling App" 🗓️
"The AI-Powered Trip Planner" ✈️
"Integrate AI Effortlessly" 🤖
"Your Finance Aid Powered by CopilotKit" 💵

This is a list i curated in 2024 as i upskilled my skills as a AI Engineer and i hope it will benefit you too on your journey in the space. LFG!!

🚀 Ready to Level Up Your Tech Insights?

Hi there! 🤗

I’m Cyprian Aarons, a Senior Software Engineer specializing in AI.

Join me on 🐦 Twitter for daily deep-dives into cutting-edge tech, behind-the-scenes looks at innovative projects, and strategies to help you thrive in the digital era.

✨ Stay Connected & Informed! ✨

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How to Ensure Your Startup Fails Spectacularly 🚀💥

CyprianTinasheAarons — Sat, 14 Dec 2024 09:43:14 +0000

Embarking on a startup journey? Here's your tongue-in-cheek guide to making sure it crashes and burns. Follow these steps meticulously, and failure is practically guaranteed. Let’s dive in!

1. Ignore the Art of Selling 🚫

Definition of Selling: Convincing someone to part with their money for your product or service.

Why It’s Crucial: Without sales, your product is just a hobby. Even the most groundbreaking innovations need a sales strategy to reach customers.

Pro Tip for Failing: Believe that a great product will sell itself. Why waste time understanding your audience when you can just assume they’ll flock to you?

2. Dismiss Marketing as Unnecessary 📢

Definition of Marketing: The art of making your product known and desirable to the masses.

Why It’s Vital: A product without marketing is like winking in the dark—you know what you’re doing, but no one else does.

Pro Tip for Failing: Think marketing is overrated. Skip the storytelling, branding, and customer engagement. Just hope the universe spreads the word for you.

3. Build Blindly Without Research 🔨

Approach: Keep building without understanding the market or customer needs.

Why It’s Doomed: You’ll end up with a product no one wants, wasting time and resources.

Pro Tip for Failing: Believe that sheer effort without direction leads to success. Keep building features no one asked for. It’s character-building, right?

4. Succumb to Analysis Paralysis 🧠

Approach: Research endlessly without ever launching.

Why It’s a Trap: Competitors will surpass you while you’re stuck in the planning phase.

Pro Tip for Failing: Think that more information always leads to better decisions. Just keep researching until your laptop’s battery dies.

5. Target a Dying Market 📉

Example: Developing a newspaper app in the digital age.

Why It’s Risky: Investing in a shrinking market limits growth potential.

Pro Tip for Failing: Assume nostalgia will drive demand. Who doesn’t miss the good old classifieds?

6. Aim for an Overly Broad Audience 🎡

Example: Attempting to solve poverty across the entire developing world with a single solution.

Why It’s Unrealistic: Lack of focus leads to diluted efforts and minimal impact.

Pro Tip for Failing: Believe that if you’re solving everything for everyone, you’re bound to succeed. The more, the merrier, right?

7. Choose a Market That Can’t Afford You 💸

Example: Selling a $10/month product to consumers with only $5 of disposable income.

Why It’s a Problem: Poor sales and eventual business failure.

Pro Tip for Failing: Assume everyone will find a way to afford your product. Maybe they’ll skip dinner?

8. Chase Every Shiny New Thing ✨

Approach: Constantly pivoting to the latest trend or tool.

Why It’s Distracting: Lack of direction and unfinished projects.

Pro Tip for Failing: Jump from one trend to another. Blockchain one day, AI the next. Who needs consistency?

9. Compete Solely on Price 🏷️

Approach: Undercutting competitors without differentiating your product.

Why It’s Dangerous: A race to the bottom erodes profits and brand value.

Pro Tip for Failing: Sell everything for peanuts. Who needs margins anyway?

10. Rely Entirely on a Single Investor’s Promise 💰

Approach: Putting all your hopes on one potential investor.

Why It’s Risky: If the investment falls through, so does your startup.

Pro Tip for Failing: Wait endlessly for that one check. Forget building traction or seeking alternative funding. Patience is a virtue, after all.

11. Isolate Yourself as a Lone Genius 🧩

Approach: Working solo, believing you don’t need a team.

Why It’s Limiting: Limited perspectives and burnout.

Pro Tip for Failing: Be the hero who does it all. Teamwork is overrated, right?

12. Pursue Only What You Enjoy 🎮

Approach: Focusing solely on personal passions, ignoring market demand.

Why It’s Risky: Creating products with no market interest.

Pro Tip for Failing: Believe your passion alone guarantees success. Customers will surely appreciate your obscure retro game software product.

13. Disregard Financial Planning 📊

Approach: Assuming revenue will naturally follow without budgeting or planning.

Why It’s Reckless: Financial instability and potential bankruptcy.

Pro Tip for Failing: Burn through your savings and hope for a miracle.

14. Lack a Clear Definition of Success 🎯

Approach: Operating without specific goals or benchmarks.

Why It’s a Problem: Aimless progress and difficulty measuring achievements.

Pro Tip for Failing: Assume success is a given. Who needs metrics or KPIs?

15. Take Reckless Risks Without Safeguards 🎲

Approach: Risking everything without contingency plans.

Why It’s Dangerous: Potential for catastrophic failure.

Pro Tip for Failing: Bet the farm. High risk, high reward… or total collapse.

16. Neglect Personal Well-being for the Startup 🛌

Approach: Sacrificing health and personal life for work.

Why It’s Unsustainable: Burnout and decreased productivity.

Pro Tip for Failing: Sleep is for the weak. Red Bull will keep you going forever.

Building a startup is hard enough without these missteps. So, take these points as a humorous reminder of what not to do. Focus, plan, and most importantly—listen to your customers. The rest will follow. 🌟

🚀 Ready to Level Up Your Tech Insights?

✨ Stay Connected & Informed! ✨

👉 📢 Follow me on Twitter

👉 📰 Subscribe to my Newsletter

🌟 Why Follow or Subscribe?

🔍 In-Depth Analysis: Get detailed insights into the latest AI and tech trends.
🎥 Exclusive Content: Access behind-the-scenes content and project updates.
💡 Actionable Strategies: Learn strategies to excel in the digital landscape.
📬 Regular Updates: Receive newsletters packed with valuable information straight to your inbox.

My 2024 Good Links List

CyprianTinasheAarons — Sat, 07 Dec 2024 07:29:15 +0000

Developer Tools & Platforms

Frameworks / Infrastructure / Backends
- Supabase – Open-source Firebase alternative; Postgres, auth, and storage.
- PayloadCMS – Headless CMS built with Node.js, TypeScript, and MongoDB.
- OpenAI Platform – API docs for building AI-driven applications.
- Langchain Python Docs – Documentation for building LLM-powered apps.
- Trigger.dev – Automated workflows with code directly in your codebase.
- Segment – Customer data platform for tracking and analytics.
- Sendgrid – Email delivery and management.
- Twilio – Communications APIs: SMS, voice, WhatsApp, and more.
- Cloudflare – DNS, CDN, and security solutions.
- Resend – Simple API for sending transactional emails.
- Upstash – Serverless data platform for Redis and Kafka.
- Openpanel – Event-level analytics for product insights.
Frontend & UI Libraries
- Tailwind UI – UI components built with Tailwind CSS.
- Shadcn UI – Customizable components built on Tailwind CSS.
- Mantine & Mantine UI – React components for building rich UIs.
- Tremor Blocks – Low-level components for building dashboards.
- Syntax UI – Tailwind-based React UI.
- Melt-UI – Svelte-based component library.
Documentation & Knowledge Bases
- Mintlify – Docs as code, easy-to-maintain documentation.
- PostHog Docs – Self-hosted analytics platform reference.
- N8n Docs – Workflow automation platform documentation.
DevOps & Deployment
- Vercel AI SDK – Tools for building AI apps on Vercel.
- Elest.io – Managed services for open-source software.
- Fleetbase API Docs – Logistics API references.
- OpenRemote – Open-source IoT platform.
AI & LLM Tools
- LlamaIndex – Data framework for LLM applications.
- FlowiseAI – Visual drag-and-drop UI to build LangChain flows.
- Serper.dev – Search APIs powered by AI.
- DreamStudio AI – Text-to-image generation.
- AI Editor – AI-assisted code and content editing.
- Continue.dev – IDE plugins for AI-driven coding assistance.
- FinancialDatasets.ai – Financial data for machine learning.
- GoAPI.ai – AI-assisted API building and testing environment.
Automation & Workflows
- Make.com Templates – Visual automation scenarios.
- Axiom.ai – Browser automation without coding.
- Cal.com Docs – Scheduling APIs and integrations.

Design & UX Resources

UI Kits & Design Systems
- UI Shadcn – Re-usable UI components for next-gen projects.
- UI Colors – Generate color palettes for UIs.
- Shapeframer – Curated shapes for Framer.
- Animata.design – Animation libraries & tools.
- Cult-UI – UI components and themes.
- Eldora UI – UI library for React projects.
- LunarUI – Elegant Tailwind-based components.
Visual Design & Inspiration
- Mobbin & Behance – Design inspiration and showcases.
- Dribbble & LandingFolio – Landing page and UI inspiration.
- Land-Book – Curated gallery of landing pages.
- Logosystem & Logggos.club – Logo design inspiration.
- Transparent Textures – Subtle background patterns.
- 3Dicons & SVG L.app – 3D and SVG icons.
- Webgradients – Curated gradients for backgrounds.
- Fontsinuse – Real-world font usage and typography inspiration.
- Minimal.gallery – Minimalist website inspiration.
- SaaSpo – SaaS landing page examples.
- UIverse.io & CSS FX – CSS components and effects.
UX & Design Articles
Tooling for Designers
- Spline – 3D design platform for web experiences.
- Figma Plugins – Extending design capabilities directly in Figma.
- HemingwayApp – Improve readability of your content.
- RealTimeColors – Live color palette adjustments.
- Polypane – Browser for responsive and accessibility testing.

Analytics, Monitoring & Marketing

Plausible.io – Privacy-friendly web analytics.
LogSnag – Event tracking and analytics.
PostHog – Product analytics and event tracking.
Mixpanel – Advanced product analytics for user behavior.
Growth.Design – Case studies on product growth and UX.
Hypefury & TweetHunter – Social media growth tools.
Nuelink – Social media automation and scheduling.
Segment – Customer data platform to unify analytics and messaging.

AI & Data

Langchain Academy – Training resources for building LLM apps.
Meilisearch & AI – AI-powered search integration.
OpenArt.ai – Browse and generate AI-generated art.
Stack AI – AI insights for data-driven decisions.
RAG (Retrieval Augmented Generation) Examples – Sample code for retrieval-augmented AI apps.
Kaggle Solutions – Curated Kaggle challenge solutions.

Workflow & Project Management

Monday.com CRM – CRM and workflow management.
ClickUp – Project management and productivity tool.
Shipfast Docs – Faster shipping solutions documentation.
Crisp.chat – Multi-channel customer support chat.
Chatwoot – Open-source customer engagement platform.
Cal.com – Scheduling infrastructure and integrations.
Make.com & Zapier – Workflow automation tools.

E-Commerce & Payments

Medusa Commerce – Commerce engine built on Node.js.
Circle Developers – Crypto payment infrastructure.
Thirdweb & Alchemy – Web3 tools and NFT infrastructure.
Atlys – Get visas and travel documents easily (service reference).

Inspiration & Learning

3Blue1Brown – Math and data visualization videos.
AIValley & DailyAI – AI news and insights.
Bootcamp UXDesign – UX and design tutorials.
Dev.to Articles – Developer-centric articles and tutorials.
Medium Publications – Articles on design, AI, web dev, and best practices.

Web3 & Blockchain

Thirdweb – Web3 development framework and tools.
Solidity Docs – Official docs for the Solidity language.
Alchemy University – Web3 development courses and tutorials.
Useweb3.xyz – Curated Web3 resources and guides.
EVM from Scratch – Learn Ethereum Virtual Machine internals.

Others & Miscellaneous

Novel.sh – AI-powered text editing.
Dyte.io – Video and voice APIs.
Acquire.com – Platform for buying and selling online businesses.
Pusher – Realtime APIs for communication features.
Framermotion Examples – Motion UI library examples.
ImageKit – Image optimization and CDN.
Testimonial.to – Collect video testimonials.

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🚀 Introduction to Building AI-Powered Apps with Streamlit and FastAPI

CyprianTinasheAarons — Wed, 16 Oct 2024 08:28:26 +0000

The software world has made it fun and exciting for builders to create solutions and bring their ideas to life quicker than ever before. Sometimes, you just want to build something cool to show off or to learn a new concept or AI model by building it. Whether it's for a quick MVP or a POC to gauge traction in the real world, learning the combo of Streamlit for the frontend and FastAPI for your backend will make you a beast at this game. 🦾

🌟 What is FastAPI?

FastAPI is a fast and modern web framework for building APIs with Python. It boasts numerous advantages, such as:

Speed: High performance, on par with Node.js and Go.
Ease of Use: Simple and intuitive syntax.
Less Code: Requires fewer lines of code to achieve the same functionality compared to other frameworks.

But there are other frameworks like Django and Flask, to name a few, that can also work well. Personally, I prefer FastAPI for many reasons, and the goal is to solve problems efficiently. So, remember to stick to one framework you like and understand. 👍

🛠️ FastAPI Basics

1. Setting Up the Virtual Environment

First, let's set up our virtual environment to keep our project dependencies isolated.

python -m venv .venv

2. Activating the Virtual Environment

Activate the environment you just created:

source .venv/bin/activate

For Windows users:

.venv\Scripts\activate

3. Installing Required Packages

Next, install FastAPI and Uvicorn (an ASGI server):

pip install fastapi uvicorn

4. Creating the Main Application File

Create a new file named main.py:

touch main.py

5. Writing the Basic FastAPI Application

Copy and paste the following code into your main.py file:

from fastapi import FastAPI
import uvicorn

app = FastAPI()

@app.get("/")
async def root():
    return {"message": "Hello World"}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

6. Running the FastAPI Application

In your terminal, run the application:

python main.py

FastAPI automatically generates documentation using OpenAPI, which is one of the many features AI engineers love! 🎉

Navigate to your local URL:

http://0.0.0.0:8000/docs

Yay!! Our first API is up and running with a simple "Hello World" response. But the fun is just beginning! 🚀

📚 Project Summary

We will be building a simple FastAPI endpoint that takes a prompt and, with the help of Replicate's hosted Flux Image GenAI model, generates a cool image. Then, using Streamlit for our client-side, we'll call the endpoint using requests to generate and present the image to the user. 🎨✨

🤖 What is Replicate.com?

Replicate lets you run AI models with a cloud API, without having to understand machine learning or manage your own infrastructure. It's a game-changer for developers looking to integrate powerful AI capabilities quickly and effortlessly.

Steps to Get Started with Replicate:

Create or Log In: Go to Replicate and create an account or log in.
Access the Flux Model: Navigate to Flux 1.1 Pro.

Replicate provides excellent documentation for their APIs:

Flux 1.1 Pro API Documentation

Select the Python documentation for seamless integration:

Obtaining Your Replicate API Token

To get the API key, follow these two steps:

Add Billing Information:
Go to Replicate Billing and follow the instructions to add your billing information.
Create API Key:
Go to Replicate API Tokens to create your API key.

Export your API token in your terminal:

export REPLICATE_API_TOKEN=<paste-your-token-here>

🖥️ Enhancing FastAPI with Replicate

Install the necessary packages:

pip install replicate loguru

Replicate: To interact with Replicate's API.
Loguru: For logging errors, information, etc. Loguru Documentation

Example Code Provided by Replicate

import replicate

input = {
    "prompt": "black forest gateau cake spelling out the words \"FLUX 1.1 Pro\", tasty, food photography",
    "prompt_upsampling": True
}

output = replicate.run(
    "black-forest-labs/flux-1.1-pro",
    input=input
)
print(output)
#=> "https://replicate.delivery/czjl/HQZ89fz0ogUSJi2RLpuTyYER..."

Customizing the Code for Our Image Generation Endpoint

Replace the above with the following enhanced code in main.py:

from fastapi import FastAPI
from pydantic import BaseModel
import uvicorn
import replicate
from loguru import logger
from typing import Dict

app = FastAPI()

# Configure Loguru to log into app.log with rotation
logger.add("app.log", rotation="500 MB")

class ImageInput(BaseModel):
    prompt: str
    prompt_upsampling: bool = True

@app.post("/generate")
async def generate_image(input_data: ImageInput) -> Dict[str, str]:
    input_prompt = {
        "prompt": input_data.prompt,
        "prompt_upsampling": input_data.prompt_upsampling
    }

    # Generate image using Replicate's Flux model
    output = replicate.run(
        "black-forest-labs/flux-1.1-pro",
        input=input_prompt
    )

    # Handle the output to get the image URL
    if isinstance(output, replicate.helpers.FileOutput):
        url = str(output)
    else:
        url = output

    return {"message": "Image generated", "url": url}

@app.get("/")
async def root() -> Dict[str, str]:
    logger.info("Root endpoint accessed")
    return {"message": "Welcome to the Flux Image Generation API"}

if __name__ == "__main__":
    logger.info("Starting the application")
    uvicorn.run(app, host="127.0.0.1", port=8000)

Code Breakdown 🧐

Importing Necessary Libraries:

from fastapi import FastAPI
from pydantic import BaseModel
import uvicorn
import replicate
from loguru import logger
from typing import Dict

Initializing FastAPI and Loguru:
```
app = FastAPI()
logger.add("app.log", rotation="500 MB")
```
- FastAPI Instance: Creates the FastAPI app.
- Loguru Configuration: Logs will be stored in app.log with a rotation size of 500 MB.
Defining the Data Model:
```
class ImageInput(BaseModel):
    prompt: str
    prompt_upsampling: bool = True
```
- ImageInput: A Pydantic model that ensures the incoming data has the required structure.

Creating the /generate Endpoint:

@app.post("/generate")
async def generate_image(input_data: ImageInput) -> Dict[str, str]:
    input_prompt = {
        "prompt": input_data.prompt,
        "prompt_upsampling": input_data.prompt_upsampling
    }

    # Generate image using Replicate's Flux model
    output = replicate.run(
        "black-forest-labs/flux-1.1-pro",
        input=input_prompt
    )

    # Handle the output to get the image URL
    if isinstance(output, replicate.helpers.FileOutput):
        url = str(output)
    else:
        url = output

    return {"message": "Image generated", "url": url}

Purpose: Takes a prompt from the user, generates an image using Replicate's Flux model, and returns the image URL.

Creating the Root Endpoint:

@app.get("/")
async def root() -> Dict[str, str]:
    logger.info("Root endpoint accessed")
    return {"message": "Welcome to the Flux Image Generation API"}

Purpose: A simple root endpoint that logs access and returns a welcome message.

Running the Application:

if __name__ == "__main__":
    logger.info("Starting the application")
    uvicorn.run(app, host="127.0.0.1", port=8000)

Purpose: Starts the FastAPI application using Uvicorn.

🎨 Building the Client with Streamlit

Now, let's create the client-side application using Streamlit to interact with our FastAPI backend.

1. Installing Streamlit and Requests

Install the necessary packages:

pip install streamlit requests

2. Creating the Streamlit Client

Create a new file named client.py and add the following code:

import requests
import streamlit as st

def generate_image(prompt: str) -> str:
    response = requests.post("http://localhost:8000/generate", json={"prompt": prompt})
    return response.json()["url"]

def main():
    st.title("🎨 Flux 1.1 Pro Image Generator")
    prompt = st.text_input("Enter a prompt for the image")

    if st.button("Generate Image"):
        if prompt:
            with st.spinner("Generating image..."):
                url = generate_image(prompt)
                st.image(url, caption="Your Generated Image")
        else:
            st.error("❗ Please enter a prompt")

if __name__ == "__main__":
        main()

3. Code Explanation 📝

Importing Libraries:
```
import requests
import streamlit as st
```
- Requests: To make HTTP requests to our FastAPI backend.
- Streamlit: To build the frontend interface.

Defining the generate_image Function:

def generate_image(prompt: str) -> str:
    response = requests.post("http://localhost:8000/generate", json={"prompt": prompt})
    return response.json()["url"]

Purpose: Sends the user's prompt to the FastAPI /generate endpoint and retrieves the generated image URL.

Creating the Main Function:

def main():
    st.title("🎨 Flux 1.1 Pro Image Generator")
    prompt = st.text_input("Enter a prompt for the image")

    if st.button("Generate Image"):
        if prompt:
            with st.spinner("Generating image..."):
                url = generate_image(prompt)
                st.image(url, caption="Your Generated Image")
        else:
            st.error("❗ Please enter a prompt")

Title: Displays the app title.
Text Input: Allows users to enter a prompt for image generation.
Button: When clicked, it triggers the image generation process.
Spinner: Shows a loading animation while the image is being generated.
Image Display: Displays the generated image with a caption.
Error Handling: Alerts the user if no prompt is entered.

Running the Main Function:
```
if __name__ == "__main__":
        main()
```
- Purpose: Ensures that the main function runs when the script is executed.

4. Running the Streamlit Client

In your terminal, run the Streamlit app:

streamlit run client.py

Navigate to your local URL, typically http://localhost:8501.

5. Generating an Image

Enter a prompt like "a car" and click on Generate Image.

Boom! Your AI-generated image appears in seconds. 🚗✨

🛠️ Quick Setup

For a seamless setup, clone the repository and follow along with the README.md:

🔗 Replicate-Streamlit-FastAPI GitHub Repository

📚 Additional Resources

🎉 We so done!!

Congratulations on getting this far! 🎊 You've just built a powerful AI-powered image generator using FastAPI for the backend and Streamlit for the frontend. Now, you can launch amazing AI solutions that will make the world a better place. 🌍✨

🚀 Ready to Level Up Your Tech Insights?

✨ Stay Connected & Informed! ✨

👉 📢 Follow me on Twitter

👉 📰 Subscribe to my Newsletter

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🔍 In-Depth Analysis: Get detailed insights into the latest AI and tech trends.
🎥 Exclusive Content: Access behind-the-scenes content and project updates.
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Building a Document QA with Streamlit & OpenAI

CyprianTinasheAarons — Tue, 15 Oct 2024 08:30:27 +0000

What is Streamlit? 🚀

Streamlit is an open-source Python framework for data scientists and AI/ML engineers to deliver dynamic data apps with only a few lines of code.

Streamlit is exciting for AI engineers who want to quickly demo or create Proof of concept projects.

Streamlit provides great documentation that is easy to understand, and for any developer to pick up easily. 📈

Some Fundamentals before we dive into our project 🧩

Installation 🛠️

To install Streamlit, we can run the following command:

pip install streamlit

To test if we have installed it successfully, we run the following:

streamlit hello

Once we have built our application script i.e <streamlit_script.py>, we can run it using the following command:

streamlit run <streamlit_script.py>

Displaying Text or Diagrams 📝

Using st.write we can display information in our app:

st.write("hello world")

Text Elements ✍️

We can display strings in different formats, e.g., markdown, title, header, and subheader:

st.markdown("*Streamlit* is **really** ***cool***.")

Widgets 🎛️

Streamlit has many widgets that include buttons, select boxes, checkboxes, etc.:

st.button("Click me")

Layout 🖼️

We can work with sidebars, columns, and expanders. For example, st.sidebar will show a sidebar on our app interface:

st.sidebar.write("I am a sidebar")

👉 Going through the Streamlit docs and cheat sheet will quickly get you updated on the entire syntax:

Hosting a Streamlit App 🌐

Hosting a Streamlit app is very easy when working with Streamlit Cloud:

Deploy Your App

Prerequisites 📋

You are a Python developer.
You have a basic understanding of Gen AI and LLMs like OpenAI.
You love learning and upskilling.
Your preferred IDE e.g VScode.

A Breakdown of our Document Question & Answer Streamlit application

We start by importing Streamlit and OpenAI into our app.py file:

import streamlit as st
from openai import OpenAI

Next, we make use of st.title and st.write to display the title and description:

st.title("📄 Document Question Answering")
st.write(
    "Upload a document below and ask a question about it – GPT will answer! "
    "To use this app, you need to provide an OpenAI API key, which you can get [here](https://platform.openai.com/account/api-keys). "
)

Next up, is the st.text_input function by Streamlit to add our OpenAI key giving our application AI capabilities:

openai_api_key = st.text_input("OpenAI API Key", type="password")

Lastly, when Implementing the core logic for the platform, we start with an if not condition to check if the key exists; otherwise, we show the st.info to ask the user to add the key:

if not openai_api_key:
    st.info("Please add your OpenAI API key to continue.", icon="🗝️")

The else condition shows our fully functional Doc QA:

else:

    # Create an OpenAI client.
    client = OpenAI(api_key=openai_api_key)

    # Let the user upload a file via `st.file_uploader`.
    uploaded_file = st.file_uploader(
        "Upload a document (.txt or .md)", type=("txt", "md")
    )

    # Ask the user for a question via `st.text_area`.
    question = st.text_area(
        "Now ask a question about the document!",
        placeholder="Can you give me a short summary?",
        disabled=not uploaded_file,
    )

    if uploaded_file and question:

        # Process the uploaded file and question.
        document = uploaded_file.read().decode()
        messages = [
            {
                "role": "user",
                "content": f"Here's a document: {document} \n\n---\n\n {question}",
            }
        ]

        # Generate an answer using the OpenAI API.
        stream = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=messages,
            stream=True,
        )

        # Stream the response to the app using `st.write_stream`.
        st.write_stream(stream)

A Breakdown of the Code 🧐

Initializing our OpenAI client using the added OpenAI key:
```
client = OpenAI(api_key=openai_api_key)
```

Using file_uploader from Streamlit, we upload our types .txt and .md:

uploaded_file = st.file_uploader(
    "Upload a document (.txt or .md)", type=("txt", "md")
)

Using text_area, we take the input from the user:

question = st.text_area(
    "Now ask a question about the document!",
    placeholder="Can you give me a short summary?",
    disabled=not uploaded_file,
)

We implement a condition to check if the user has uploaded a file and inputted a question:
```
if uploaded_file and question:
```
We read the file and process what the user uploaded:
```
document = uploaded_file.read().decode()
```

We initialize the messages and pass them to our OpenAI chat completions endpoint:

messages = [
    {
        "role": "user",
        "content": f"Here's a document: {document} \n\n---\n\n {question}",
    }
]

# Generate an answer using the OpenAI API.
stream = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=messages,
    stream=True,
)

Finally, using write_stream, we stream the output:
```
st.write_stream(stream)
```

Setting Up the Project Locally on your machine 🏗️

Clone the repository:

git clone git@github.com:CyprianTinasheAarons/document-qa.git
cd document-qa/

Create a virtual environment:

python3 -m venv venv

Activate the environment:

source venv/bin/activate

Install the requirements found in requirements.txt:

pip install -r requirements.txt

Yay!! Now we can run our code:

streamlit run streamlit_app.py

We navigate to our local URL and add our OpenAI key:

Get your API key here: OpenAI API Keys

🎉 Conclusion

Congratulations on getting this far! Now you can go and launch great AI solutions that will make the world better! 🎊

Feel free to follow me on Twitter for more updates and projects. Also, check out my website here. 🌐✨

📚 Resources

🚀 Ready to Level Up Your Tech Insights?

✨ Stay Connected & Informed! ✨

👉 📢 Follow me on Twitter

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🌟 Why Follow or Subscribe?

🔍 In-Depth Analysis: Get detailed insights into the latest AI and tech trends.
🎥 Exclusive Content: Access behind-the-scenes content and project updates.
💡 Actionable Strategies: Learn strategies to excel in the digital landscape.
📬 Regular Updates: Receive newsletters packed with valuable information straight to your inbox.