@upstageai document Parse extracts structured text from PDFs/DOCs with top performance. This is essential for tasks like LLM training or RAG. https://
console.upstage.ai/docs/capabilit
ies/document-digitization/document-parsing
… It accurately recognizes tables and charts, converting them into structured text. Try it—it’s excellent.
LLMS
-
Upstage Document Parse: AI-Powered PDF Extraction for LLM Training
By
–
-
Gemini Achieves Gold Medal in Math Olympiad, Launches 2.5 Flash-Lite
By
–
ICYMI here’s what shipped this week —Gemini achieved gold-medal standard in the International Mathematical Olympiad
—Gemini 2.5 Flash-Lite is stable and generally available for developers and enterprise customers
—You can now turn photos into videos in @GooglePhotos and -
Upstage Document Parse Extracts Structured Text from PDFs
By
–
@upstageai 's document Parse extracts structured text from PDFs/DOCs with top performance. This is essential for tasks like LLM training or RAG. https://
upstage.ai/products/docum
ent-parse
… It accurately recognizes tables and charts, converting them into structured text. Try it—it’s excellent. -
Qwen’s New Thinking Model Takes 166 Seconds to Process
By
–
Qwen released their updated "thinking" model today. It thinks really hard! Took 166 seconds to think through the details of drawing me a pelican on a bicycle. The finished drawing wasn't great but the thoughts behind it were fun to see. https://
simonwillison.net/2025/Jul/25/qw
en3-235b-a22b-thinking-2507/
… -
Ambient Agents Will Dominate 2025 as Models Achieve Long Autonomy
By
–
ambient agents are going to completely dominate the rest of 2025: 1. human deep work/focus requires at least 1-2 hrs uninterrupted 2. by EOY all nextgen models* will pass the 1-2hr autonomy METR barrier ∴ they will be used in completely different ways than the current
-
LLM Docker Container Integration for Autonomous Task Automation
By
–
My end goal is to give an LLM a Docker container and the ability to run shell commands and be able to have it Do Stuff there – edit files, run commands etc – but I know that's wildly ambitious
-
Running Devstral on 64GB M2 Mac with Ollama
By
–
64GB M2 Mac, might be able to run Devstral well on it, I only tried that in Ollama so far, need to try other harnesses to see if they support tools more reliably
-
Qwen Code Model Exceeds Mac Memory Capacity Limitations
By
–
Sadly the Qwen Code model doesn't fit on my Mac! I only have 64GB
-
Models Making Multiple Tool Calls Single Conversation
By
–
It's really the number of tool calls you can make a single conversation Many models can do one just fine, but I want to be able to do a dozen or more