TRANSFER LEARNING book [390 pages]: http://
amzn.to/3R4G0zm Amazon Summary:
"Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to
SYSTEMS
-
Transfer Learning Book: 390 Pages on Adaptive Systems
By
–
-
AI Performance Is an Infrastructure Issue, Not Just Software
By
–
The PGA Championship is a useful reminder for every technology leader: AI performance is not just a software issue. It is an infrastructure issue. Before asking what AI can automate, predict, or recommend, leaders should ask whether their network can support those decisions
-
HarnessX self-compiles; Anthropic and Manus remove complexity iteratively
By
–
HarnessX: a harness that compiles itself. every harness improvement so far has come from a human editing code by hand. Anthropic strips planning steps out of Claude Code when a stronger model ships. Manus rebuilt its agent five times in six months, removing complexity each
-
Frustration about missing tool prompts in published system prompts
By
–
Looks like that's the search tool usage instructions – they still don't share tool prompts in their published system prompts which continues to be frustrating
-
Scalable Voice Agent Design with Amazon Nova Sonic and BedRock
By
–
Scalable Voice Agent Design with Amazon Nova Sonic with Amazon BedRock: Multi-Agent, Tools, and Session Segmentation! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang
-
LangSmith Engine: The Agent of Agents
By
–
LangSmith Engine: The agent's agent. pic.twitter.com/0oBp3rM2AX
— LangChain (@LangChain) 16 juin 2026LangSmith Engine: The Agent of Agents
-
Agent: successful response but task failure
By
–
An agent can return a "successful" response and still fail at the task. It may call the wrong tool, omit an approval step, use the wrong context, or produce a response that seems correct but is not. That is why teams
-
Traces and Evals: From Debugging to Continuous Improvement Loop
By
–
Traces show the inputs, model calls, tool calls, outputs, and final action. Evals turn those learnings into a way to test whether the next version is better. This is how teams move from manual debugging to a continuous improvement loop. Join @hwchase17 for a deep dive on June
-
Google unveils TimesFM, a zero-shot predictive AI
By
–
GOOGLE HA LIBERADO EN SILENCIO UNA IA QUE PREDICE PATRONES
— Nico (@nicos_ai) 16 juin 2026
Ventas. Precios de mercado. Tráfico web.
Demanda energética. Volatilidad cripto.
Se llama TimesFM:
→ Entrenada con 100B de datos reales
→ Forecasting zero-shot, sin fine-tuning
→ Corre en local.
100% Gratis y Open… pic.twitter.com/shKaFPJuxhGOOGLE HAS QUIETLY RELEASED AN AI THAT PREDICTS PATTERNS Sales. Market prices. Web traffic.
Energy demand. Crypto volatility. It's called TimesFM: → Trained on 100B of real data
→ Zero-shot forecasting, no fine-tuning
→ Runs locally. 100%
— -
Stellar performance of a 3B model through post-training refinements
By
–
Stellar performance from a 3B model. These results were achieved primarily through post-training refinements on Qwen2.5-Coder. The paper doesn't provide many details, but it appears they distill from RL ckpts and then do a final RL-based instruct RL. https://
arxiv.org/abs/2606.16140