Vandam C Bramdito

a good man, geographic technique specialist

[Avi Chawla @_avichawla]

DevOps vs. MLOps vs. LLMOps, clearly explained:

Many teams are trying to apply DevOps practices to LLM apps.

But DevOps, MLOps, and LLMOps solve fundamentally different problems.

DevOps is software-centric. You write code, test it, and deploy it. The feedback loop is straightforward, i.e., does the code work or not?

MLOps is model-centric. Here, you're dealing with data drift, model decay, and continuous retraining. The code might be fine, but the model's performance can degrade over time because the world changes.

LLMOps is foundation-model-centric. Here, you're typically not training models from scratch. Instead, you're selecting foundation models and then optimizing through three common paths:

- Prompt engineering
- Context/RAG setup
- Fine-tuning

But here's what really separates LLMOps: The monitoring is completely different.

In MLOps, you track data drift, model decay, and accuracy.

In LLMOps, you're watching for:

- Hallucination detection
- Bias and toxicity
- Token usage and cost
- Human feedback loops

This is because you can't just check if the output is "correct." You need to ensure it's safe, grounded, and cost-effective.

The evaluation loop in LLMOps also feeds back into all three optimization paths simultaneously. Failed evals might mean you need better prompts, richer context, OR fine-tuning.

So it's not a linear pipeline anymore.

One more thing: prompt versioning and RAG pipelines are now first-class citizens in LLMOps, just like data versioning became essential in MLOps.

And the ops layer you choose should match the system you're building.

If you want to go deeper into LLMOps, I wrote a full LLM engineering roadmap a while back.

It walks through the eight pillars of building LLM systems, starting at prompt engineering and ending at observability and safety, with free and open-source resources attached to each one.

You can read it below.

[kaori @kaorixbt]

Google just released a free 2-hour course on complete Graph Engineering

How to go from one prompt to agent teams, loops, and self-building graphs:

10:16 - Build your first AI agent
41:05 - Master prompt engineering
54:45 - Turn agents into graphs
1:20:10 - Run loops inside graphs
1:43:33 - Build a graph that improves itself

Prompts → Agents → Loops → Graphs → Self-Improving Systems

Most people ship one agent and stop there

Google is already teaching everything that comes next

This free course is better than most paid agent engineering programs

Watch it today

Then read how to run 1,000 agents from one prompt below ↓

[codila @0xCodila]

Anthropic's Andrej Karpathy just released 1-hour Stanford lecture on full AI engineering from scratch:

"You can actually delete everything… Delete everything, keep Graph"

here's his lessons:

10% → LLM: I treat GPT as a general-purpose computer that can be reprogrammed at runtime

30% → Prompt: I give that computer a program written in natural language

50% → Agent: I surround the model with a goal, context, memory, and tools that turn prediction into action

70% → Loop: I separate the inner loop, where the model learns from context, from the outer loop, where training updates its weights

100% → Graph: I organize communication as data-dependent message passing over directed graphs

This 1-hour at Stanford will teach you more about AI than 100 YouTube video guides

“Just chop up everything and throw it into the mix”

watch - bookmark, then read the article below ↓

[Roan @RohOnChain]

Don't waste 2 years learning to become an AI engineer in 2026.

Google just gave the complete playbook on full AI agent engineering.

2 hour course. Free:

• 00:00 - build your first AI agent
• 45:38 - multi agent architecture
• 55:30 - AI agents with MCP tools
• 1:30:45 - AI agent loop engineering
• 1:39:54 - AI agent graph engineering

I watched it last night.

Halfway through, I realized I could break into Anthropic in weeks, not years.

Bookmark now. Watch it. Then build your own AI agent with the guide below.

[Roan @RohOnChain]

Don't waste 2 years learning to use LLMs like Claude & ChatGPT.

Andrej Karpathy, the godfather of AI, dropped a 2 hour course on how he personally uses LLMs daily.

• 00:00 - LLMs simplified
• 22:49 - which LLM to actually use
• 42:00 - one prompt for full research
• 1:13:57 - how to code with LLMs
• 1:37:04 - NotebookLM podcast generation

This course will teach you more about using LLMs than most AI engineers learn in their entire career.

Bookmark this & give 2 hours today, no matter what. Then read the article below.

[Mahax @Mahaximus_]

Google Brain founder, Andrew Ng:

"Prompting will die in 6 months.

Loops and Graphs are what's replacing it."

In 2 hours, he shows exactly what the best engineers already build instead, and how to start building it yourself.

The missing piece most people skip: how to connect those loops into a graph that compounds every time it runs.

Watch it, then read the full guide on loops and graphs below.

[Lunar @LunarResearcher]

Ex-Google engineer just released a free 3-hour course on building and monetizing AI agents.

How to go from one agent to a full system that finds leads and makes money:

00:00 - Design an AI agent system
07:38 - Add human handoffs
19:27 - Understand RAG and vector databases
52:38 - Deploy agents to Google Cloud
1:25:57 - Turn agents into a paid WhatsApp business
1:33:00 - Convert conversations into leads
2:24:01 - Learn loops vs graphs
2:33:08 - Build a multi-tool agent graph

Most people are still building AI demos.

This course shows the full stack:

Agents → RAG → Deployment → Leads → Revenue

Building agents is the old workflow.

Monetizing agent systems is the new one.

This 3-hour watch covers more than most $500 paid courses.

Save it before everyone starts selling the same thing.

[Movez @0xMovez]

Don't waste 2 years figuring out how AI agents actually work.

ex. Anthropic & Google engineers at Stanford just dropped a 1-hour lecture on self-improving AI agents:

• 00:00 - Introduction to self-learning AI systems
• 31:22 - inside a LLM: self-correction, backtracking
• 42:24 - goal → loops → graphs→ feedback: the Agent loop
• 45:47 - 6 agent orchestration patterns
• 50:37 - the generator-verifier gap - why self-improvement stalls

Planning, multi-step reasoning, self-correction. That's the entire agent roadmap for 2026.

Bookmark this & give 70 minutes today. Then read the article below.

[Clodex @0xClodex]

instead of watching 2 hours of Netflix tonight, watch this Stanford lecture

it's the clearest explanation I've seen of how ChatGPT and Claude actually work

useful whether you've never touched AI in your life or have been using it every day for the past year

i took the key ideas and turned them into a practical guide on how to actually get 100% out of AI

you can find it below with ready-to-copy prompts and solutions

[Anatoli Kopadze @AnatoliKopadze]

instead of watching 2 hours of Netflix tonight, watch this Stanford lecture

it's the clearest explanation I've seen of how ChatGPT and Claude actually work

useful whether you've never touched AI in your life or have been using it every day for the past year

i took the key ideas and turned them into a practical guide on how to actually get 100% out of AI

you can find it below with ready-to-copy prompts and solutions

[Science girl @sciencegirl]

A personalised lamp

[Inferam @InferamAI]

How to do push-ups the right way.

[Out of Context Human Race @NoContextHumans]

Tom Cruise doing his own stunts

[Felt @felt]

Learn how to publish @QGIS maps to the web with the ‘Add to Felt’ plugin. https://t.co/m7YN2tDo83 Styled rasters and vectors import seamlessly! 🗺️

#GIS #GISchat #OSGeo

[Amirhossein Ahrari @Enginn3r]

Google #EarthEngine Tutorials about #Climate Data Analysis:
- Precipitation Downscaling: https://t.co/yXXGqFyoYn
- #Temperature Downscaling: https://t.co/l3WhxQ2FqC
- #Precipitation Mapping: https://t.co/VJrYaGbIL2
- #Drought Mapping: https://t.co/zgZxyQMbpc

[Vandam C Bramdito @vandamcaesar]

@dadimdum Kalau Hire asisten bisnis development dulu gimana? Untuk memulai dulu.. Komandonya Via WA dan telepon..

[Vandam C Bramdito @vandamcaesar]

Nokia 3510i 2004
Nokia 7250 2005
Nokia 6155 2007
Nokia N72 music edition 2008
Nexian NX G911 2009
CSL Blueberry 8800 2010
Samsung Galaxy Y S5360 2011
Blackberry Curve 9220 Davis 2012
Asus Zenfone 5 A500CG 2015
Xiaomi Redmi Note 4x 2017
Xiaomi Poco X3 Pro 2021 hingga sekarang

[Vandam C Bramdito @vandamcaesar]

China dan Rusia tolong lindungi IRAN pake Hak Veto juga di PBB, Palestine 🇵🇸 Iran🇮🇷 #Iran #Palestine