Codex will be integrated further into ChatGPT
LLMS
-
GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization
By
–
GPU Forecasters Language Models as Selective Surrogates for Kernel Runtime Optimization
-
New paper reveals AI model scaling laws based on bytes, not tokens.
By
–
A new paper just exposed a setting that changes how AI models scale.
— AlphaSignal AI (@AlphaSignalAI) 2 juin 2026
Scaling laws tell labs how big a model should be for a given amount of data.
Until now, that math was always done in tokens.
A new paper rewrites the rule in bytes.
The team trained 988 models, from 50M… pic.twitter.com/G7j5tySh9VA new paper just exposed a setting that changes how AI models scale. Scaling laws tell labs how big a model should be for a given amount of data. Until now, that math was always done in tokens. A new paper rewrites the rule in bytes. The team trained 988 models, from 50M
-
LangSmith LLM Gateway: Spend Limits with 402 Response
By
–
LangSmith LLM Gateway lets you set spend limits. You can set them at the org, workspace, user, or API key level. When a cap is hit, the agent receives a 402 response with a clear error.
-
OpenAI’s Codex reaches 4M weekly users, 5x growth since February
By
–
OpenAI is on a winning streak: Codex passed 4M weekly users, 5x since February. Knowledge workers are now a fifth of them, growing 3x faster than developers. The tool OpenAI built for coders is being adopted fastest by people who don't code. All figures from OpenAI's own
-
Seeking thoughts on Opus 4.8 after tepid reception
By
–
Almost a week later! What are your thoughts on Opus 4.8?
— Dan Shipper 📧 (@danshipper) 2 juin 2026
We were extremely bullish on it in testing—it seems the response was more tepid once y'all got your hands on it. If you disagreed with our take I'm curious why so we can tune our evaluations!
One theory I have is that by… https://t.co/nBUhzUbYRKAlmost a week later! What are your thoughts on Opus 4.8? We were extremely bullish on it in testing—it seems the response was more tepid once y'all got your hands on it. If you disagreed with our take I'm curious why so we can tune our evaluations! One theory I have is that by
-
Most AI Agents Fail in Production Because They’re Built Backward
By
–
Most AI Agents Fail in Production Because They’re Built Backward! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming
-
AI Engineering Toolkit: 100+ Libraries for LLMs, RAG, AI Agents
By
–
AI Engineering Toolkit! I have curated a list of 100+ libraries and frameworks for training, fine-tuning, building, evaluating and deploying LLMs, RAG, and AI Agents. Categories of LLM Libraries include: • Vector Databases – Store and retrieve embeddings efficiently.
• -
Paper’s artifact claim: editable skill files, not agent action, behavioral fidelity frontier.
By
–
Right, that gap is the paper's own point. It claims an artifact (editable skill files), not that the agent acts on the judgment, and calls it the behavioral fidelity frontier, with no fidelity eval.
