Pai Buabthong

Director, Nakhon Ratchasima Office of Lifelong Learning (NRLL) @NRRU_official 👨‍🏫 | ML and graph-based learning | Adjn. Prof. @CMKLUniversity @KMUTT

[GitHub Projects Community @GithubProjects]

MaxKB is an open-source platform for building enterprise-grade RAG agents with an integrated pipeline for knowledge retrieval and AI workflows.

- RAG pipeline with automatic text splitting, vectorization, and document ingestion
- Agentic workflow engine with function library and MCP tool-use capabilities
- Model-agnostic support for private (DeepSeek, Llama, Qwen) and public (OpenAI, Claude) models
- Zero-coding integration into third-party business systems

[Thariq @trq212]

https://t.co/Ljzw0lmvao

[Bryan Johnson @bryan_johnson]

Two weeks without mobile internet improved mental health more than antidepressants and reversed roughly 10 years of attentional decline.

Screen time dropped 49% (314 to 161 min/day).

[Pai Buabthong @paippb]

@lukepierceops Agency

[Pai Buabthong @paippb]

General election tmr. 🤞🤞

[Pai Buabthong @paippb]

@aryanXmahajan ENGINE

[Aryan Mahajan @aryanXmahajan]

This AI Agent replaces your $200K/Year Marketing team

while I was eating shawarma at 2am, it scraped 6 platforms, analyzed 73 comment threads, and handed me 9 deploy-ready scripts.

It stalks your niche, steals what’s working, and turns it into viral content built to print attention.

here’s what the system does:

– scrapes TikTok, IG, LinkedIn, YouTube, FB, and Twitter
– finds breakout trends, emotional triggers, and dopamine hooks
– maps audience avatars + repurposing angles
– generates platform-native scripts with hooks, CTAs, SEO intros
– formats everything into a deploy-ready content playbook

no more brainstorming. no more “what should we post today?”

it’s like giving your intern the mind of Alex Hormozi - on a 400mg caffeine drip.

the workflow includes:

- Multi-platform scraper engine
- Viral comment sentiment analyzer
- GPT-powered creative generator
- Smart repurposing module
- Auto-formatted Google Docs output

this isn’t ChatGPT with a cute prompt.

it’s a weaponized idea machine.

if you’re serious about content — and tired of guessing — this flips the game.

Comment “ENGINE” + repost this + follow me
I'll DM you everything in the next hour

skip this, and go back to scheduling posts that get 4 likes.

[v0 @v0]

Introducing GitHub sync on v0:

• Push generated code to GitHub directly from v0
• v0 will automatically pull changes from GitHub into your chat
• Switch branches and open PRs to collaborate with your team

[Carlos E. Perez @IntuitMachine]

It turns out that Anthropic has a prompt engineering interactive course!

[Pai Buabthong @paippb]

@damianplayer n8n

[Pai Buabthong @paippb]

Consistently over-function in environments that under-function will quickly drains you, reinforces weak systems around you, and feeds a loop of frustration masked as duty.

[Pai Buabthong @paippb]

Isan Next Seminar

[Pai Buabthong @paippb]

Presenting a vision for research infrastructure in 2050 at ICRI 2024

[Muratcan Koylan @muratcan]

I've built it for you!!

It's an automated AI system that analyzes AI case studies (you can change the use case) to identify and document enterprise-level AI implementations.

It starts by reading URLs from a CSV file and uses web scraping (either through WebLoader or Firecrawl) to extract the content from each case study.

The extracted content is then sent to Claude 3.5 Sonnet, which analyzes whether the case study represents a genuine enterprise AI implementation based on specific criteria like company maturity, implementation scale, and measurable business outcomes.

For each URL, the system first saves the raw content and then performs this initial qualification analysis.

If Claude determines that a case study qualifies as an enterprise AI implementation, the system proceeds to generate a detailed analysis.

It creates three types of reports:
- an individual case study report with sections like Executive Summary, AI Strategy Analysis, and Business Impact Assessment
- a cross-case analysis that identifies patterns and trends across multiple case studies
- and an executive dashboard summarizing key metrics and insights.

All of these reports are saved in structured formats (markdown for individual reports, JSON for cross-case analysis and dashboard) in their respective directories.

If a case study doesn't qualify as an enterprise AI implementation, the system logs the reason and moves on to the next URL.

The entire process is asynchronous and provides detailed terminal feedback about its progress and decisions.

[Rohan Paul @rohanpaul_ai]

THE BEST visual explainer of how information propagates through a transformer.

If you want to have more than intuition about how the Transformer architecture is ruling the LLM world

→ open-source project explains everything about LLM Transformer Models!

→ A great resource for anyone looking to gain a deeper understanding of how Transformer-based AI models like GPT work, including:

→ Self-attention mechanisms
→ Encoder-decoder architecture
→ Positional encoding
→ Multi-head attention

[Sumit @_reachsumit]

Long Context RAG Performance of Large Language Models

Databricks analyzes 20 LLMs and reveals that only recent state-of-the-art models maintain consistent RAG accuracy above 64k tokens, with most models' performance declining at longer contexts.

https://t.co/LklY4fPs9s

[Pai Buabthong @paippb]

Night science 🌃

[Itai Yanai @ItaiYanai]

Science has a hypothesis-testing part, but it has an equally crucial ‘night science’ mode, where new ideas are improvised for the first time.

[Natia Kurdadze @natiakourdadze]

I read all 222 pages of @levelsio 's book “Make: Bootstrapper’s Handbook” and here’re my notes on all 9 chapters: 👇

[Pai Buabthong @paippb]

NRRU's graduate programs are now back to be managed by each department (used to be centralized)