4/ The main reason I keep this running is that it is local-first. Everything runs on my own device, so there is no cloud dependency or data leakage risk. For anyone non-technical who wants to automate tasks without learning how to use a Terminal, it completely scales your token
TOOLS
-
AI agent builds zero-coding tools to automate repetitive tasks
By
–
3/ Having an AI agent build functional tools with zero coding required completely changes how repetitive work gets done. It pulls multi-source feeds, strips out layout clutter, and refreshes instantly, saving me from manually opening and jumping between 15 different browser tabs
-
Building a news dashboard with KroWork’s thought process
By
–
1/ I finally tried building my own tool with KroWork to see if this "Chat-to-software" thing is as easy as they say.
— Chubby♨️ (@kimmonismus) 21 mai 2026
I asked the agent, Kro, for a news dashboard, and updating to the latest version lets you see the full KroWork thought process mapping out the system logic. It… pic.twitter.com/vC6LPMym2D1/ I finally tried building my own tool with KroWork to see if this "Chat-to-software" thing is as easy as they say. I asked the agent, Kro, for a news dashboard, and updating to the latest version lets you see the full KroWork thought process mapping out the system logic. It
-
Controlling LoRA Strength and Combining LoRAs in Image Tools
By
–
once the LoRA is trained, you can use it on the Image tool.
— Krea (@krea_ai) 21 mai 2026
you can control its strength and combine multiple LoRAs together. pic.twitter.com/90PJ6ONvYdonce the LoRA is trained, you can use it on the Image tool. you can control its strength and combine multiple LoRAs together.
-
Google TurboVec: Shrink Vector Embeddings from 31GB to 4GB
By
–
Google's new algorithm just shrunk 31GB of vectors into 4GB. Storing embeddings for RAG eats memory fast. Ten million documents in float32 takes 31 GB of RAM. A new open-source Rust vector index changes that math. TurboVec fits the same corpus into 4 GB. It runs on
-
DreamLite: ByteDance’s compact 0.39B model for fast image generation and editing
By
–
What if your phone could generate or edit images in under a second? ByteDance’s Intelligent Creation Lab presents DreamLite, a compact 0.39B parameter model that unifies text-to-image generation and editing in one network. It uses a simple trick: concatenating images
-
Free course on delivering AI-powered analytics and dashboards
By
–
Modern analytics needs more than dashboards. It needs a new foundation. This free one-hour course covers:
• Delivering trusted, AI-powered insights using unified data, governance, business logic, and the lakehouse
• AI/BI Dashboards, Genie spaces, semantic layers, and data -
Introducing LoRAs for Krea 2: Powerful AI Fine-Tuning System
By
–
introducing LoRAs for Krea 2 (beta).
— Krea (@krea_ai) 21 mai 2026
our most powerful fine-tuning system to date; now you can train Krea 2 on a your own specific style, object, or character with incredible precision.
learn how it works 👇 pic.twitter.com/pbsuw43pMvintroducing LoRAs for Krea 2 (beta). our most powerful fine-tuning system to date; now you can train Krea 2 on a your own specific style, object, or character with incredible precision. learn how it works
-
Frameworks and Prompts to Validate an AI MVP
By
–
7 prompts. 3 frameworks. One Perplexity deep research session. You go from "what should I build?" to validated MVP scope with a launch plan. Before writing a single line of code. I built these from Christensen's Jobs-to-Be-Done and Kim & Mauborgne's Blue Ocean Strategy. The
-
Apply JTBD Demand Detector to an AI Opportunity
By
–
Prompt 5: "The JTBD Demand Detector" "For the strongest opportunity you identified, apply
Clayton Christensen's Jobs-to-Be-Done framework. Research what 'job' the target user is actually hiring
a product to do. Not features. The underlying progress
they're trying to make.