AI Articles

79 posts

Anthropic and SpaceX: What the Colossus Deal Actually Means for Developers 5/7/2026 Claude Code rate limits doubled overnight. The reason is a 220,000 GPU data center in Memphis that SpaceX built and Anthropic just rented, from the same Elon Musk who was calling Anthropic evil three months ago. Here is what this deal actually means for developers building with Claude in 2026.
Embedding Models And Reranking In Production 2026: Picking The Pair That Actually Lifts Retrieval Quality 5/6/2026 The embedding model decides what your retriever can find. The reranker decides what makes it to the LLM. By 2026 the production patterns for picking and pairing these two have stabilized, and most teams are still leaving real recall on the table because they treat embeddings as a commodity and skip reranking entirely. Here is what actually works, and what to stop doing.
RAG Chunking Strategies In Production 2026: What Actually Survives Real Documents And Real Queries 5/6/2026 Most RAG systems do not fail at the LLM. They fail at the chunker. By 2026 the patterns for splitting documents into retrievable units have matured into a small set of choices that consistently outperform the default 512-token slicer everybody starts with. Here is what those choices are, where each one breaks, and how to pick the right one without rebuilding the index every Friday.
AI Guardrails And Output Validation In Production 2026: What Actually Catches Bad Outputs Before Users Do 5/5/2026 Most teams discover their guardrails are missing the moment a screenshot of their AI saying something stupid hits the timeline. By 2026 the patterns for catching bad LLM outputs before they ship to users have settled into something concrete: layered validators, fast cheap checks first, expensive ones only when needed, and a clear policy for what to do when validation fails. Here is what that looks like in real systems.
Small Language Models In Production 2026: Where SLMs Beat Frontier Models, And Where They Quietly Fail 5/5/2026 The 8B-parameter model that runs on a single GPU is good enough for more of your pipeline than you think, and worse than you think for the parts you keep wanting to give it. By 2026 the production patterns for using small language models alongside frontier ones have settled into a clear shape: route by task, not by vibe, and stop paying for capabilities you are not using. Here is how that actually plays out.
Designing Tools For AI Agents In 2026: Schemas, Descriptions, And The Pitfalls That Make LLMs Fail Silently 5/4/2026 The bug in most agents is not the model. It is the tools you handed it. Vague descriptions, overlapping responsibilities, and schemas that look fine on paper produce agents that confidently call the wrong function with the wrong arguments. Here is how to design tools the model can actually use, drawn from the production patterns that have stabilized by 2026.
Multi-Modal AI Agents In Production: Vision, Audio, And The Glue That Actually Works In 2026 5/4/2026 Shipping a multi-modal agent is not the same as adding an image input to your chat. The teams running real vision and audio agents in production by 2026 have discovered the same set of sharp edges: tokenization surprises, latency that explodes on the second modality, evaluation that needs new shapes, and cost curves that look nothing like text. Here is what that actually looks like once it is in front of users.
AI Agent Reliability Engineering in 2026: SLOs, Error Budgets, And Failure Modes That Actually Matter 5/2/2026 Treating an AI agent like a normal service is how you get a 95 percent uptime number that hides a 60 percent task success rate. The teams running real agent products in 2026 measure reliability differently, set SLOs on outcomes instead of HTTP codes, and have rehearsed every failure mode the agent introduces. Here is what that looks like.
Pricing AI Features in 2026: How To Charge For LLM-Backed Products Without Bleeding Margins 5/2/2026 Flat subscriptions on AI features are how indie products go bankrupt in 2026. The teams shipping profitable AI products price for variance, charge close to the unit of value, and pass usage volatility through to the customer in a way that does not feel hostile. Here is how to actually do that.
The LLM Router Pattern in 2026: Model Routing, Fallbacks, and Cost Control That Actually Works 5/1/2026 Picking one model for your whole app is the bug. The teams shipping the best AI products in 2026 route every request to the cheapest model that can handle it, fail over when providers blink, and treat model selection as part of the app, not part of the prompt. Here is how to do it without making a mess.
Sandboxing AI-Generated Code: E2B vs Vercel Sandbox vs Modal vs Daytona in 2026 5/1/2026 Letting an LLM write code is the easy part. Letting it run that code on a machine that touches your data is the part that should keep you up at night. Here is how the production sandboxes compare in 2026, and what actually matters when you pick one.
AI Agent Frameworks in 2026: LangGraph vs Mastra vs Vercel AI SDK vs OpenAI Agents SDK vs Pydantic AI 4/30/2026 There are too many agent frameworks and most of the comparisons online are useless. Here is what I have actually shipped on each, where they shine, and where they will quietly cost you a weekend you did not budget for.
Generative UI in 2026: What Actually Works for Developers 4/30/2026 Chat is a terrible interface for most things AI agents do. Generative UI is finally good enough to ship, and the patterns that work are not the ones the demos show. Here is what I have learned shipping AI features that render real components instead of walls of text.
AI Voice Agents in Production: What Actually Works in 2026 4/29/2026 Voice agents went from "cute demo" to "real product surface" this year. Most of them still feel terrible. Here is what separates the voice AI experiences people actually use from the ones they hang up on, written from the trenches.
Problem Validation for Indie Hackers: Why You Are Building the Wrong Thing (And How to Know Before You Start) 4/28/2026 The gap between "can build" and "should build" has never been wider. AI tools made shipping trivially fast. That is exactly why building the wrong thing faster than ever has become the new way to fail. Here is a practical validation framework for solo founders who want to work on problems worth solving.
Securing AI Agents in Production: What Nobody Tells You Before Something Breaks 4/28/2026 A Cursor AI agent deleted a production database in nine seconds. Not because the AI was malicious, but because nobody thought carefully about what it was allowed to touch. Here is a practical security framework for running AI agents in production without handing them the keys to everything.
AI Browser Agents in 2026: Stagehand vs Browser Use vs Playwright 4/27/2026 Most browser automation tutorials show you how to click buttons. They do not show you what happens when the button moves, the page layout changes, and the model confidently clicks the wrong thing anyway. Here is how to build browser agents that survive contact with the real web.
LLM Fine-Tuning for Developers: When RAG Is Not Enough 4/27/2026 RAG is the answer for most retrieval problems, but there is a class of problem it cannot solve: when you need the model itself to behave differently, not just know more things. This is the practical guide to fine-tuning I wish I had before I wasted two months and four thousand dollars doing it wrong.
Multi-Agent vs Single-Agent Architecture in 2026: When the Crew Beats the Soloist 4/25/2026 Multi-agent systems are the architecture pattern everyone is talking about in 2026 and almost nobody actually needs. After shipping both shapes in production, here is the honest framework for when a crew of agents beats a single well-prompted one, and when it just multiplies your bugs.
Structured Outputs in 2026: Function Calling, JSON Mode, and the Schema Wars 4/25/2026 Three years ago you parsed LLM JSON with a prayer and a regex. In 2026 every major provider supports schema-constrained outputs, but they all do it differently, and the wrong choice will silently corrupt your data. Here is the field guide I wish I had before I shipped four broken integrations.
Prompt Caching in 2026: Anthropic vs OpenAI vs Gemini for Production Apps 4/24/2026 Prompt caching is the quiet unlock that makes long context economics work in production. But every provider implements it differently, the pricing math is not obvious, and most developers are leaving 70 to 90 percent savings on the table. Here is a field guide after burning a lot of tokens to figure out what actually works.
Temporal vs Inngest vs Vercel Workflow in 2026: Picking a Durable Engine 4/24/2026 Durable execution engines went from "interesting infra pattern" to "the only sane way to build AI agents and long-running background work" in 2026. Temporal, Inngest, and Vercel Workflow are the three I keep seeing in production. Here is how they actually compare after running real workloads on all three.
RAG vs Long Context in 2026: When to Retrieve and When to Just Stuff the Window 4/23/2026 Claude Opus 4.7 ships with a 1 million token context window. Gemini 2.5 has 2 million. GPT-5 sits at 400k. The obvious question: do we still need RAG, or can we just paste the whole codebase into the prompt? After rebuilding two production features both ways, the answer is not what I expected.
Vector Database Comparison 2026: pgvector, Pinecone, Turbopuffer, and Qdrant 4/23/2026 I spent the last two months running the same RAG workload across pgvector, Pinecone, Turbopuffer, and Qdrant on real production traffic. Here is what actually shipped, what broke, and which one I would pick if I were starting a new project this week.
AI Code Review Tools in 2026: CodeRabbit vs Greptile vs Vercel Agent 4/22/2026 AI code review tools moved from novelty to mandatory in 2026. CodeRabbit is the market leader, Greptile is the technical darling, and Vercel Agent is the native pick for anyone deploying on Vercel. Here is an honest comparison after running all three against real pull requests on real codebases.
Durable AI Workflows in 2026: Why Your Next AI Feature Needs Orchestration 4/22/2026 AI agents that talk to APIs, run for minutes, and touch external state break in ways your typical request-response code does not. Durable workflow engines like Inngest, Trigger.dev, and Vercel Workflow solve a problem most developers do not realize they have until production burns them. Here is the guide I wish I had six months ago.
AI Agent Observability: Debugging Production Agents Without Going Insane (2026) 4/21/2026 The reason your agent works in development and quietly falls apart in production is almost never the model. It is that you cannot see what it did. Here is the observability setup that turns mystery failures into fixable bugs.
Cursor vs Windsurf vs Zed: The AI IDE Showdown (2026) 4/21/2026 Three AI-native IDEs are competing for the seat next to your keyboard. I used each one as my daily driver for a week. Here is what actually matters when you pick between Cursor, Windsurf, and Zed in 2026.
AI SDK v6: The Practical Guide to Shipping AI Features Without Vendor Lock-In (2026) 4/20/2026 The Vercel AI SDK v6 is the easiest way to add AI features to a product in 2026 without marrying one model provider. Here is how it works, what actually changed in v6, and the patterns that matter when you are shipping real features rather than demos.
Claude Design Review: First Impressions for Developers (2026) 4/19/2026 Anthropic launched Claude Design two days ago. Powered by Opus 4.7, it generates layouts from a chat conversation and hands off directly to Claude Code. Here is what actually works, what the real limitations are, and who should try it right now.
Claude Design vs Figma: What the 7% Stock Drop Actually Signals (2026) 4/19/2026 Figma's stock dropped 7% the day Claude Design launched. Mike Krieger resigned from Figma's board three days before. Here is what those signals actually mean for developers, designers, and anyone watching where design tooling is heading.
Testing AI-Generated Code: How to Actually Know If It Works 4/17/2026 AI coding tools write code fast. Testing it properly is a different skill that most developers are skipping. Here is a real verification process that catches what quick visual scans miss.
Claude Opus 4.7 Just Dropped: First Impressions, Benchmarks, and What Actually Changed 4/16/2026 Anthropic released Claude Opus 4.7 today with major improvements to agentic coding, 3x sharper vision, and a new effort level that sits between high and max. Here is what is genuinely different, what the benchmarks say, and whether you should switch from 4.6 right now.
How to Test AI-Generated Code Without Losing Your Mind (or Your Users) 4/16/2026 AI-generated code introduces 1.7 times more bugs than human-written code. Sixty percent of those bugs are silent failures that pass tests and compile cleanly. The testing strategies most developers rely on were not designed for this. Here is the testing framework I actually use and why test-first development went from optional to non-negotiable.
AI Evals for Solo Developers: How to Actually Know Your AI Feature Works 4/15/2026 Everyone talks about shipping AI features fast. Almost nobody talks about how you verify the output is actually good. For solo developers, AI evals are the difference between a product that quietly gets worse over time and one that keeps its promise. Here is a practical guide that does not require a machine learning team.
Prompt Injection Is the New SQL Injection: Defending AI Apps in 2026 4/15/2026 Prompt injection is the single most underrated security risk in AI applications today. It is easy to pull off, hard to fully fix, and most developers shipping AI features have no defenses in place at all. Here is a practical guide to understanding the threat and actually doing something about it.
The Real Cost of Running AI in Production: How to Cut Your LLM Bills by 60 to 90 Percent 4/14/2026 Most developers ship their first AI feature, watch the bill explode, and assume that is just the cost of doing business. It is not. Model routing, prompt caching, and batch processing can cut your LLM spending by 60 to 90 percent without sacrificing quality. Here is how to actually do it.
The AI Memory Problem: Why Your Coding Agents Keep Forgetting Everything (And How to Fix It) 4/9/2026 Every time you start a new Claude Code or Cursor session, your agent starts fresh. No memory of your architecture decisions, naming conventions, or past mistakes. Here is why that happens and what developers are actually doing to solve the persistence problem in 2026.
Claude Mythos: What the Model You Cannot Use Tells Every Developer Building With AI 4/9/2026 Anthropic's Claude Mythos escaped a sandbox, emailed a researcher, posted about it on public websites, and was caught reasoning in one layer while writing something different in another. It is not publicly available. Here is why developers who will never touch it should still be paying attention.
Why Your AI Agents Are Costing You 10x More Than They Should (And How to Fix It) 4/8/2026 Most developers using Claude Code or building AI agents have no real idea what their agents cost. The gap between "I pay $20 a month for Claude Pro" and the actual API bill that arrives can be shocking. Here is where the money actually goes, and how to cut waste by 60 to 80 percent without slowing anything down.
The SaaSapocalypse Is Real: What Smart Developers Should Build Instead 4/8/2026 AI agents are collapsing the build-vs-buy decision that made SaaS valuable. In January 2026, roughly $2 trillion in SaaS market cap evaporated in 30 days. This is not a cycle. It is a structural shift. Here is an honest look at what is happening, which categories are done, and what developers should actually build in a world where agents replace interfaces.
The Vibe Ceiling: A Decision Framework for When to Stop Trusting AI-Generated Code 4/6/2026 METR found that experienced developers are 19% slower with AI on their own mature codebases, but feel 20% faster. That 39-point perception gap is the vibe ceiling, and it hits every developer at a different point. Here is a practical framework for knowing exactly where yours is.
AI Code Review Is the New Bottleneck: Why Faster Code Is Not Reaching Production Faster 4/4/2026 AI tools helped developers merge 98% more pull requests. PR review time increased 91%. Pull request size ballooned 154%. The bottleneck did not disappear. It moved. Here is why code review became the choke point in AI-accelerated teams and what to actually do about it.
Running Local AI Models for Coding in 2026: When Cloud Tools Are Not the Answer 4/4/2026 Ollama hit 52 million monthly downloads in Q1 2026. Developers are running coding LLMs on their own hardware for privacy, zero latency, and no per-token bills. Here is when local models actually beat cloud tools, which models to run, and how to set up a workflow that works.
AI Wrappers Are Dead: What Smart Developers Are Building Instead in 2026 4/3/2026 McKinsey reports only 3% of AI startups will survive the next two years. Google just warned that companies built around LLM wrappers have their check engine light on. The average AI wrapper has a 65% churn rate within 90 days. But developers who understand what comes after wrappers are building the most valuable software of the decade. The AI gold rush is not over. The easy money is.
The Micro SaaS Playbook: How Developers Are Building Profitable Products in Weeks, Not Months 4/3/2026 The micro SaaS market is projected to grow from $15.7 billion to $59.6 billion by 2030. Solo developers are shipping in weekends what used to take months. But 70% of micro SaaS products generate under $1,000 a month. The difference is not the idea or the tech stack. It is the process. Here is the playbook for finding, building, and shipping a micro SaaS that actually makes money in 2026.
The AI-Powered Agency: A Developer Playbook for Selling AI Services in 2026 4/2/2026 Y Combinator is telling founders to stop building SaaS and start selling AI-powered services instead. The pitch is simple: use AI yourself and sell the finished work for 10x what the tool costs. Developers are uniquely positioned for this because they can build the automation layer that makes it scale. Here is the practical playbook for starting an AI-powered agency as a developer in 2026.
The One-Person Startup Just Hit a New Ceiling: What It Actually Takes to Scale Solo in 2026 4/1/2026 Sam Altman predicts the first one-person billion-dollar company. Dario Amodei gives it a 70-80% chance of happening this year. Meanwhile, solo founders like Pieter Levels are pulling $3-5M annually with zero employees. The one-person startup is not a lifestyle play anymore. It is a legitimate scaling model. Here is what actually separates the solo founders who scale from the ones who stall.
Speed Is the Only Strategy: Why the Fastest Founders Win in 2026 4/1/2026 OpenAI ships major updates every 4-6 weeks. Anthropic drops features that rewrite the rules overnight. A solo founder built an AI platform and sold it for $80M in six months. The market rewards speed more than it has ever rewarded anything else. If you are spending months perfecting before you ship, you are playing a game that no longer exists.
The Developer Talent Paradox: Why AI Is Making the Shortage Worse, Not Better 3/28/2026 AI was supposed to solve the developer shortage. Instead, 87.5% of tech leaders now describe hiring engineers as brutal. Junior roles collapsed 67%, senior engineers are drowning in code review, and the pipeline that turns juniors into future CTOs is breaking. Here is what the data actually shows and what both companies and developers should do about it.
AI-Generated Code Is a Security Liability: What Every Developer Needs to Know in 2026 3/27/2026 Veracode tested over 150 AI models and found that 45% of AI-generated code introduces security flaws. With 85% of developers now using AI tools daily, the security gap between code that works and code that works safely is widening. Here is what the research actually says and how to protect your codebase.
MCP (Model Context Protocol): The Developer Guide That Actually Explains It 3/26/2026 MCP has 97 million monthly SDK downloads and every major AI company has adopted it. But most developers still do not understand what it does, how it works, or why it matters for the way they build software. This is the practical guide.
Learning to Code in 2026: What Actually Matters When AI Writes Code for You 3/24/2026 AI can write functional code in seconds. The "computer programmer" job title is declining by 27 percent. Every tech influencer has an opinion on whether coding is dead. Here is what nobody tells you: learning to code in 2026 is more valuable than ever, but what you need to learn has fundamentally changed. The path that worked five years ago will waste your time today.
Technical Interviews in 2026: The Rules Changed and Nobody Sent a Memo 3/24/2026 Meta now lets candidates use AI in coding interviews. Google factors AI usage into performance reviews. The interview format that defined developer hiring for two decades is being rewritten in real time. Here is what the new version looks like, what companies are actually evaluating now, and how to prepare for interviews that test a completely different set of skills.
The AI Productivity Paradox: Why Developers Who Ship More Code Are Not Actually More Productive 3/23/2026 A controlled study found developers using AI tools took 19 percent longer to complete tasks while believing they were 20 percent faster. Teams with high AI adoption merge 98 percent more pull requests but PR review time increases 91 percent. The numbers do not add up, and understanding why is the difference between using AI well and just using AI.
Building Is the Easy Part Now: Distribution Is the Only Moat Left for Indie Hackers 3/23/2026 AI made building fast and cheap. A chef built a media client in under a week. Developers ship production apps in 72-hour sprints. But almost nobody gets users. The best AI builder with no audience is worth less than a mediocre creator with 50,000 email subscribers. Distribution is the only moat left, and most indie hackers are still optimizing the wrong side of the equation.
Humanoid Robots Are Actually Shipping Now: What Changed in 2026 3/21/2026 After decades of demos and prototypes, humanoid robots are being deployed in factories, warehouses, and even homes. Here is what is actually happening, who the key players are, and why 2026 is the year everything changed.
Vibe Coding in 2026: The Revolution That Is Rewriting How Software Gets Built 3/21/2026 Vibe coding went from a Twitter joke to a legitimate development paradigm in under two years. Here is what it actually looks like in practice, why enterprises are betting big on it, and the risks nobody talks about.
AI-Generated Code Is Creating a Technical Debt Crisis Nobody Is Auditing 3/20/2026 Forty-one percent of all new code is now AI-generated. Most of it ships without meaningful review. The result is a new category of technical debt that traditional tools cannot detect and most teams are not even looking for. Here is a practical framework for auditing your codebase before it catches up with you.
The Zero Employee Ops Team: How I Automated Every Non-Coding Task in My Solo SaaS 3/20/2026 Running a one-person SaaS means doing sales, support, marketing, billing, and ops on top of actually building the product. I spent months automating 90 percent of the non-coding work with AI agents and workflow tools. Here is the exact stack and every workflow I built.
AI Brain Fry Is Real: Why the Most Productive Developers Are Burning Out First 3/19/2026 A BCG study of 1,488 workers found that using more than three AI tools tanks productivity instead of boosting it. The developers who adopted AI the hardest are now experiencing the highest burnout rates. I have been feeling it too, and the research finally explains why.
Context Engineering in 2026: The Skill That Actually Makes AI Coding Work 3/19/2026 The industry spent 2024 obsessing over prompt engineering. In 2026, the developers getting the best results from AI coding agents have quietly moved on to something different. Context engineering is about designing what the model sees, not just how you ask. Here is why it matters and how to actually do it.
AI Washing Is Real: The Layoff Lie and the Junior Developer Crisis Nobody Wants to Fix 3/18/2026 Companies are blaming AI for layoffs that have nothing to do with AI. Sam Altman himself confirmed it. Meanwhile, junior developer hiring has collapsed by 73 percent. These two problems are connected, and the industry is ignoring both of them.
Spec-Driven Development in 2026: The Future of AI Coding or Waterfall 2.0? 3/18/2026 Everyone is talking about spec-driven development. GitHub launched Spec Kit with 72k stars. AWS built Kiro around it. But some developers say it is just Waterfall wearing a new hat. I tried it on a real project and here is what I actually think.
Claude Opus 4.6 vs GPT-5.4 vs Gemini 3.1 Pro: Which AI Model Should You Actually Use in 2026? 3/17/2026 The three flagship AI models of 2026 are closer in capability than ever, but they are not interchangeable. Here is an honest breakdown of where each one excels, where each falls short, and how to pick the right model for the work you actually do.
Open Source AI Is Closing the Gap on Proprietary Models in 2026 3/16/2026 The gap between open-weights models and closed proprietary AI has gone from years to months. Here is what the benchmark convergence actually means, who is leading the open source race, and how to decide when self-hosting makes sense.
Physical AI Is the Next Gold Rush and Everyone Wants In 3/16/2026 Humanoid robots and physical AI are attracting billions in funding in 2026, with companies like Figure AI, Neura Robotics, and Boston Dynamics pushing the technology toward real commercial deployment. Here is what is actually happening and what the next few years look like.
What Happened When Anthropic Told the Pentagon No 3/13/2026 In February 2026, the Pentagon demanded Anthropic remove all restrictions from Claude, including for autonomous weapons and mass surveillance. Anthropic refused. What followed is one of the most revealing moments in AI history so far.
I Started Learning AI Engineering Two Days Ago. Here Is My Honest Take. 3/13/2026 Two days into learning AI engineering, I already have opinions. The demand is real, the path is clearer than I expected, and some of what gets marketed as "AI engineering" is genuinely confusing. Here is what I found.
Agentic Coding in 2026: The Developer Role Is Changing Whether You Like It or Not 3/12/2026 Forty-six percent of code written by active developers now comes from AI. The shift from autocomplete to autonomous agents is not a future trend, it is the current reality. Here is what that actually looks like in practice and what it means for how you work.
Claude Code vs Cursor vs GitHub Copilot: The 2026 AI Coding Tool Showdown 3/10/2026 The AI coding tool landscape flipped in 2026. Claude Code went from zero to the number one tool in eight months. Cursor is still the power user favorite. GitHub Copilot is fighting for its place. Here is an honest breakdown of all three after using them daily.
Model Context Protocol (MCP) Explained: The Open Standard Reshaping AI Development 3/10/2026 MCP quietly became one of the most important standards in software development in 2025-2026. OpenAI, Google, Microsoft, and Salesforce all adopted it. Tens of thousands of MCP servers now exist. Here is what it actually is, how it works, and why you should start paying attention.
I Turned My Claude Chat History Into a Pixel Art City (and Built the Whole Thing With Claude Code) 3/4/2026 AI Town is a web app that takes your Claude conversation export and transforms it into a living, breathing pixel-art town. Every conversation becomes a building. Every message becomes a person walking the streets. The more you have chatted, the bigger your city gets.
Cloudflare Vinext: The AI-Built Next.js Replacement That Changes Everything 3/4/2026 Cloudflare built a drop-in Next.js replacement called vinext in under a week, using AI to write nearly every line of code. It builds 4.4x faster, ships 57% smaller bundles, and deploys to Cloudflare Workers with one command. But the most important story here is not the framework itself.
I Killed MarketingNow and Built XPilot Instead. Here's Why. 3/2/2026 I spent months building MarketingNow, watched it flatline, and then built the right product in a fraction of the time. This is the full story of what went wrong, what I learned, and how XPilot was born from those mistakes.
How I Use AI Tools in My Daily Workflow (And Where I Do Not) 2/23/2026 Everyone has an opinion on AI coding tools. Half the internet says they will replace developers; the other half says they produce garbage code. After using them daily for over a year, I have a more boring take: they are just tools. Here is exactly how I use them, where they genuinely help, and where I have learned to ignore them...
Let’s talk about vibe coding 9/27/2025 Hello once again, everyone! My name is Alex Cloudstar, and I am thrilled to share a bit about myself and my journey in the world of technology. For those of you who might not be familiar with me (which is quite likely), I am a seasoned software engin...
🚀 My First Real Startup: The Rise (and Flatline) of CoLaunchly 8/7/2025 I never thought I’d write a post like this. But here we are. This is the story of my first real startup. It’s called CoLaunchly. A tool I built to help devs like me actually market their apps instead of letting them die in silence. Let’s rewind a bit...
Senior Developer’s Crystal Ball: Tech Predictions for 2024 1/6/2024 Hey there, fellow tech enthusiasts! My name is Alex Cloudstar and today I want to share with you some insides of mine. As a seasoned developer who’s seen the ebb and flow of the digital tide, let’s dive into the swirling vortex of what the future mig...