Building AI Systems That Hold Up in Production — The Cost of Getting It Wrong: https://
odbms.org/blog/2026/06/t
he-cost-of-getting-it-wrong-ivan-santa-maria-filho-on-building-ai-systems-that-hold-up-in-production/
… via @odbmsorg Quote from article: "the most important lever is to experiment first, find exactly how AI will be used and whether it is the most cost effective way to solve
SYSTEMS
-
Cost of getting AI wrong: experiment first for production systems
By
–
-
Tackling Anthropic’s 319-page System Card
By
–
So far, the minimum we need to know. Now it's time to tackle the System Card of only 319 pages 🙂 Link https://www-cdn.anthropic.com/d00db56fa754a1b115b6dd7cb2e3c342ee809620.pdf …
-
Which Local AI Model Would You Run on Your Infrastructure?
By
–
LOCAL AI MODELS Which model would you run on your own infrastructure? Qwen 3.7 Max DeepSeek V4 Gemma 3 GPT OSS Different strengths: Privacy Control Cost efficiency Performance What are you running today? #AI #LocalAI #LLM #Ollama #OpenSourceAI
-
NVIDIA explains AI Grid basics for telecom networks
By
–
Earlier this year we announced that telecom leaders are building AI grids using NVIDIA AI infrastructure to optimize inference on distributed networks
— NVIDIA (@nvidia) 9 juin 2026
But what actually is an AI Grid?
In this video, Amogh Dendukuri takes us back to basics. Watch now to see him break down the… pic.twitter.com/JMgh8YXq9FEarlier this year we announced that telecom leaders are building AI grids using NVIDIA AI infrastructure to optimize inference on distributed networks But what actually is an AI Grid? In this video, Amogh Dendukuri takes us back to basics. Watch now to see him break down the
-
Anthropic system card PDF link shared by @arrakis_ai
By
–
System card : https://
www-cdn.anthropic.com/d00db56fa754a1
b115b6dd7cb2e3c342ee809620.pdf
… -
Unified platform for training and testing world models
By
–
You can now train and test world models in one place. Researchers kept rebuilding the same machinery for world models. These systems let agents imagine outcomes before acting. Every project shipped its own training stack and data loader. Nothing was comparable.
-
Self-evolving agent framework where agents learn and rewrite skills
By
–
Let Agents Design Agents! Memento-Skills is a self-evolving agent framework where agents learn from failures and rewrite their own skills. Most agent frameworks treat skills as static. You write them once, load them into context, and hope they work. When they fail, you debug
-
Loops move judgment earlier; gates hold most value
By
–
this is the cleanest framing: loops move judgment earlier. the work shifts from “write the next prompt” to “define memory, tools, gates, stop rules, and review”, the gate is where most of the value lives
-
Quality and budget gates fix two failure modes; brakes matter
By
–
Exactly, quality gates and budget gates solve two different failure modes which are bad work getting through, and okay-ish work running forever most agent loop demos show the motion, the useful part is the brakes
-
Apple’s new 20B on-device AFM 3 for iPhone 17 Pro
By
–
Apple's new foundation models are genuinely exciting. The standout is AFM 3 Core Advanced, a 20-billion (!) parameter model that runs entirely on-device. Read that again. 20-billion, on-device, iPhone 17 Pro. It pulls this off by keeping the full model in flash memory and