I Stopped Self-Hosting AI: Why DeepSeek V4 Pro on Ollama Cloud Is My New Default

27 June 202627 June 2026 by susiloharjo

I Stopped Self-Hosting AI: Why DeepSeek V4 Pro on Ollama Cloud Is My New Default

The most-said line in my group chats this week was three words: “I miss Fable.”

Not in a nostalgic way. In a “my entire workflow is broken” way.

Fable was the model I used for first-draft generation. Fast, cheap, good enough for 80 percent of the work. Then it vanished. No deprecation warning. No migration path. Just gone.

My first reaction was what a lot of people are doing now: go local. Buy a GPU, run llama.cpp, never depend on a vendor again. I spent $1,400 on a used RTX 4090. I downloaded 150GB of model weights. I learned to love the sound of my fans spinning at 80 percent.

For one month, self-hosting worked. Then the novelty wore off.

The 4090 draws 450W under load. My electricity bill went up $35. The 70B models I was running maxed out at 32K context — not enough for full codebase reviews. Batch processing hundreds of documents meant queuing jobs overnight. And when Opus 4.8 dropped with significantly better reasoning, I had no way to access it without going back to cloud anyway.

I was renting infrastructure, not avoiding vendors. The landlord just changed from Anthropic to NVIDIA.

Then I tried DeepSeek V4 Pro on Ollama Cloud. The pricing made me reconsider everything.

Opus 4.8 Plans, Gemini 3.5 Executes — I Sit in Middle

24 June 202624 June 2026 by susiloharjo

For the last six weeks I have been running my project work through a two-agent loop, and it has changed how I think about AI assistants. Opus 4.8 plans. Gemini 3.5 executes. I sit between them as the human in the loop, and the work gets faster and cleaner than any single-agent setup I have run before.

This is what the flow looks like, what each model is actually good at, and where the loop breaks when I push it too hard.

RAG Retrieval Is Filtering, Not Search.

24 June 202624 June 2026 by susiloharjo

I have been building RAG pipelines for two years. The mental model I started with was wrong, and reading Angela Shi’s article “Retrieval Is Filtering, Not Search” on Towards Data Science this week made the fix click.

The standard framing of RAG retrieval is “find the passages most similar to the query.” That framing is misleading because it imports the wrong mental model. Retrieval is not a Google-style search across unstructured text. It is a filtering problem on structured tables. The closer mental model is a SQL query, not a Google search.

This is the article that should have existed when I started. Here is what I learned, and what I am changing in my own RAG pipelines because of it.

AI Wrote 80% in 10 Minutes. The Last 20% Took 6 Hours.

24 June 202624 June 2026 by susiloharjo

Abstract dark coding keyboard representing AI-generated code

I shipped a feature on a Tuesday that took 11 minutes end-to-end. The agent generated the happy path, ran the tests, opened the PR. I clicked merge. Done before lunch.

The same agent shipped a feature on a Friday that took me 6 more hours after the agent finished. The happy path looked identical. The difference was the last 20%.

That gap is what this post is about.

Claude Code vs Cursor 2026: The Honest Comparison

23 June 202623 June 2026 by susiloharjo

SpaceX is reportedly buying Cursor for $60 billion. Anthropic is shipping Claude Code updates every two weeks. Every developer I know is asking the same question: which one should I actually use?

I spent the last 90 days shipping production code with both. Not toy projects. Not benchmarks. Real features, in a real codebase, with real deadlines. Here’s what each one is actually good at — and where they both fail you.

I’m not going to give you a feature table. You’re smart enough to read the docs yourself. What I am going to do is tell you what happened when I made each tool do real work.

My AI Coding Agent Kept Breaking — What I Changed

23 June 202623 June 2026 by susiloharjo

Six weeks ago, my AI coding agent was producing garbage. Not bad code — garbage. Functions that compiled but did nothing. Tests that passed for the wrong reasons. Refactors that introduced three bugs while fixing one.

I spent two days debugging the agent. Then I spent a week rebuilding it. Then I realized the problem wasn’t the agent.

The problem was me.

This is the story of what I changed. Not the agent — me.