udomsak chundang

Dear follower, this is my personal account and it's not a promote campaign nor SEO or news feed. Sometime may have some nude photograph i'm apologize. But i'm kind of gentle and respect no sexual harassment. Thank you. #me

Automates complex reasoning tasks with extensible tools github.com/octotools/octo…

Zero-config multi-agent AI coding setup github.com/lee-to/ai-fact…

Agentic AI System Architecture

this TTS model generates speech 167x faster than you can hear it. Supertonic is an on-device TTS engine that runs via ONNX for cross-platform inference. - no GPU - 31 languages - captures every emotion - beats ElevenLabs on speed - runs even on a Raspberry Pi 100% open-source.

Generates scientific figures from research papers and text descriptions github.com/ResearAI/AutoF…

Builds a knowledge graph from AI conversations github.com/nowledge-co/no…

We asked the CEO of HuggingFace @ClementDelangue what the risks of releasing powerful open source models are. He says restricting AI creates more risk than openness. "Six, seven years ago, at the time it was GPT-2, and there was already a lot of people saying that it was too dangerous to release in open source." "Mythos, when it was announced was crazy dangerous... In a few weeks or a few months, everyone is gonna be using Mythos, and not destroy the world as a result." "For cybersecurity, the biggest risk is that a few players have capabilities that other people don't have... If you make it more open, it's usually easier for defenders to react and make the whole system safer." "The idea of restricting a technology like AI based on risks is like saying, 'Some people can punch other people, so let's tie down everybody's hands.'" "Otherwise you slow down progress, you create massive gaps in terms of controls, in terms of capabilities, and you create actually additional risks."

Reverse-engineers Android apps with LLMs github.com/zinja-coder/ap…

@akshay_pachaar amazing. you should try out this : github.com/Salomondiei08 a workfllow i built around hermes that sets up specialized agents for software engineering

Orchestrates coding agents across multiple devices and models github.com/Runfusion/Fusi…

One command. Your entire AI skill stack. Installed. github.com/midudev/autosk…

Continuously updates network diagrams from live infrastructure scans github.com/netvisor-io/ne…

Curated list of long-term memory research and tools for LLMs github.com/IAAR-Shanghai/…

Advice for AI engineers 💡 In 2026 audio to function call can be solved with Small Models. This is how you build local, fast and private voice assistants... that work :-) Here's a full example with code⭣ github.com/Liquid4All/coo…

Turns text prompts into vector UI designs github.com/ZSeven-W/openp…

Holy shit... a team just open sourced an AWS emulator that runs the entire cloud on your laptop with 13 MiB of memory. It's called Floci and it boots 45 AWS services in under a second, no Docker, no LocalStack subscription, no $30/mo dev environment bills. Every AWS dev tool before this needed Docker, gigabytes of RAM, and 30 second cold starts just to test a Lambda function. Floci is a single Go binary that runs the entire AWS stack in memory and starts faster than your terminal can render the prompt. Here's what makes it different from every AWS emulator that came before: → 13 MiB total memory footprint, the average Chrome tab uses 200x more RAM than this entire AWS clone → 45 services emulated locally including S3, Lambda, DynamoDB, SQS, SNS, IAM, CloudFormation, Step Functions, all in one binary → Sub-second cold start, your tests finish before LocalStack even pulls its Docker image → Zero dependencies, no Docker daemon, no Python runtime, no Java VM, just one Go executable → Drop-in compatible with AWS SDK and CLI, point your endpoint at localhost and every existing script works untouched Killed: $40/mo LocalStack Pro, every AWS dev environment burning $200+/mo on staging accounts, Docker Desktop eating 4GB of RAM just to run a fake S3 bucket. Pre-built binaries for Linux, macOS, Windows. No install scripts, no config files, no setup wizard. Download the binary, run it, your local AWS is live. Works in CI pipelines where spinning up Docker containers takes longer than the actual tests. 100% Opensource.

100+ agents, commands, and templates for Claude Code github.com/davila7/claude…

The choice between StatefulSet and Deployment in Kubernetes looks small on paper but tends to surface real problems once a database or message queue is involved. The article below walks through both controllers and the cases where each one actually fits inside a Kubernetes cluster. The MySQL primary and replicas example is the part that makes the distinction click IMO, showing why predictable Pod names like mysql-0 matter when applications need to find the primary node. Discussion on ordered scaling, persistent volume claims, and headless Services are included as well. Emmanuel Fordjour Kumah has put together a good side by side breakdown and a short summary that ties stateless versus stateful thinking back to controller choice. Check it out! lckhd.eu/GFdlFi #Kubernetes #StatefulSets #deployments

Fully open sources training data for 30B scale search agents github.com/PolarSeeker/Op…

Organizes files and photos using tags instead of folders github.com/TagStudioDev/T…