Wth?! @$+％&!!!??? How Did I Lost That?!😡Fine! A Deal Is A Deal . Heres A Little Gift For My Dearest Fan❤️ Be Grateful! 😤 /// Pokemon Canari Set B June⚡ Ending In 3 More Days! #pokemonlegendsza# #pokemoncanari# #dyn4mo#

Nick Emmanwori signing autographs after watching the end of 6th #Seahawks# training-camp practice. He remains on the physically-unable-to-perform list following arthroscopic ankle surgery this month. Coach Mike Macdonald terms it “an adverse situation” for his dynamo on defense

Most agentic stacks run into the same problems pretty quickly: reasoning and tool parsing drift across turns, KV cache reuse falls apart, or tools fire too late. We’ve been hardening Dynamo’s harness-facing path so @Claudeai Code, @OpenClaw, and @openai Codex-style agent patterns behave reliably on custom stacks and inference endpoints: • Stable prompts for KV reuse and lower TTFT • Interleaved reasoning + tool calls preserved across turns • Streaming tool dispatch instead of end-of-turn buffering • Harness behavior aligned with real multi-turn agent runtimes If you’re building your own agent stack or serving endpoint, this blog goes through the infrastructure issues that tend to show up in practice and the patterns we’ve been using to fix them. Tech blog ➡️

Open weights are table stakes now So here's the boring infra nobody wants to tweet about, also open⚡️ Things we've open sourced: — CubeSandbox, per-conversation sandboxes with browser, code, shell and file access, spun up and torn down for you — Agent Memory, a shared memory layer across your agents, and now across your teammates' agents too Things we've upstreamed: — RoCE optimizations for DeepSeek's DeepEP, doubling throughput on commodity networks, with 30% carrying back to InfiniBand — FlexKV, tiered KV cache offloading, now in NVIDIA Dynamo, vLLM and TensorRT-LLM mainlines All four are searchable by name, repos and docs. Pull requests welcome, and so are complaints 🛠️

TL;DR Reliable agentic AI comes not from "a better model or prompt" but from explicitly engineering context and the orchestration harness—lessons from PRINCE, a Bayer × Thoughtworks pharma system. 🧪 Title: Building Reliable Agentic AI Systems URL: Highlights 🧭 Sequential agents with pause points: clarify intent → Think & Plan → Researcher → Reflection → Writer, verifying step by step 🔁 Three reflection loops: process (trajectory), data (evidence sufficiency), draft (output completeness) catch distinct failures 🔎 Hybrid retrieval: query expansion n=5, weighting 0.7 semantic + 0.3 keyword, bge-reranker cutting ~20 → 7 chunks 🗃️ Structured data via Text-to-SQL: SELECT-only, up to 3 self-corrections, ≤50 rows per query 🛟 Harness engineering: state persisted in PostgreSQL/DynamoDB, resume from failure point, automatic provider fallback 📌 Sentence-level citations plus RAGAS and Langfuse evals; daily batch checks on live traffic catch hallucinations 🏷️ NER extracts entities from study PDFs; high-confidence fields auto-update, low-confidence quarantined for human review The "even with big context windows, selectivity still matters" stance rings true to anyone shipping this stuff. #AIAgents# #LLMOps#

Amazon Web Services (AWS) says it will take several months to fully restore its cloud services in the Middle East. The data centers in the UAE and Bahrain were badly damaged by incidents in early March, this affected many key services, including EC2, S3, DynamoDB, and RDS. In an update, AWS said some repairs have been done, but the work is taking a long time because the damage to the buildings and equipment is very serious. Customers are asked to move their important work to other regions right away and use their disaster recovery plans with backups stored somewhere else.