Timer XL: A Long-Context Foundation Model for Time-Series Forecasting! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux
LLMS
-
Elon Musk announces multiple Grok models training in Colossus 2
By
–
We have many great Grok models training simultaneously in Colossus 2. I am confident that they will bear fruit. And work on the Grok Built harness is progressing well.
-
Curious user asks Grok to explain all references
By
–
I’m curious- @grok explain all those references
-
Counterfactual scenarios of AI development and failures
By
–
If Llama 4 didn’t fail, if Microsoft had pulled Sydney after the Roose article, if New Sonnet hadn’t been so good, if Orion hadn’t been so meh, if the leadership change at OpenAI had happened, if a recession had hit in 2024, if ChatGPT-3.5 & GPT-4 hadn’t both been leaps etc etc
-
Claude’s ‘slop’ wins AI Hold Steady lyric challenge
By
–
“She said the theme of this party is the industrial age. And you came in dressed like a train wreck.” Asking AIs to think of the equivalent to this Hold Steady lyric, but for AI. Claude was the clear winner: "…And you came in dressed like slop."
-
Speculative Decoding Enables 2-3x Token Decoding Speedups
By
–
Good call out. Speculative decoding turns decode into a verification step, so one memory read covers multiple tokens instead of just one. In prod the wins depend on draft model quality and acceptance rate. With a well-matched draft, 2 to 3x speedups are realistic. Covering
-
Optimizing Prompt Length and Caching in AI Models
By
–
Thanks, glad it landed. For TTFT, the first thing I check is prompt length and whether the prefix is stable enough to hit prompt caching. Most wins come from trimming the system prompt and keeping volatile content at the end, not from model-level changes.
-
Clarification on AI Model Prefill and Decode Compute Phases
By
–
Small correction: prefill is compute-bound, decode is the memory-bandwidth-bound phase. But the KV cache point is spot on. It grows linearly with context, and every decode step has to read the whole thing.
-
Techniques to Improve AI Sequence Decoding Efficiency
By
–
Yes, and it is linear in sequence length, so the pain shows up fast. This is why GQA, sliding windows, and quantized caches exist. They all attack the same problem from different angles: keep the cache small so decode stays fast.
