Just a few years ago we were not allowed to say "sigmoid" on this app without a fear of getting canceled.
LLMS
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Codex for Windows launches with native app and PowerShell sandbox
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Codex for Windows is here! > Native Codex app on Windows via the Microsoft Store
> Local PowerShell execution with a native Windows sandbox
> WSL support, but not required
> Full app features: Skills, Automations, Worktrees, review, parallel threads
> Windows-native setup for -
Codex AI’s Approximation Method Sparks Frustration Over Benchmarking
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Now it looks like Codex was not necessarily OPTIMIZING certain algorithms for specific hardware, it was using APPROXIMATIONS instead to speeding things up. And all the benchmarking scripts might have been just the smokescreen. TWO WEEKS OF COWORK DOWN THE DRAIN!!!! AAARRRGH!!!
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Anthropic’s one-click export system for ChatGPT/Gemini to Claude import is genius
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Anthropic created a one-click system that allows users to export history from ChatGPT or Gemini into a single code block for instant import into Claude. Genius move.
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Complete Roadmap to Master Agentic AI Development
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Roadmap to learn Agentic AI AI fundamentals
Python + frameworks
LLMs
Agents architecture
Memory + RAG
Planning & decision-making
RL & self-improvement
Deployment
Real-world automation
Agentic AI = full-stack intelligence.
Credit: Tiksly
#AgenticAI #LLM #RAG #A -
DeepSeek and Qwen3 performance improvements
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specific numbers worth sitting with: > DeepSeek-R1-7B on MATH-500: 93% accuracy (up from 91.6%), tokens cut from 3,871 to 2,141 > DeepSeek-R1-1.5B on AIME 2025: accuracy jumps 6.2 percentage points > Qwen3-8B: response length halved from 18,342 to 9,183 tokens with no accuracy
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SAGE: Efficient Step-by-Step Reasoning
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their solution: SAGE (Self-Aware Guided Efficient Reasoning). instead of generating token by token, SAGE extends chains in whole reasoning steps. after each step, it checks: is the model confidently signaling it wants to stop? if yes, reasoning ends. no fine-tuning. no new
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Will Large Models Replace Traditional Machine Learning for Tabular Data?
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There's one question that persists: will conventional ML still remain relevant, given the advancement of large models, which can very much analyse the tabular data and make models out of it? There can be multiple arguments here that large models may outperform, or it might be just not good enough. But the major issue would be actual deployment in real world system where they need to work with tabular data and generate the outcomes as fast as possible. if it can do then traditional ml can be bypassed easily imo
→ View original post on X — @avikumart_, 2026-03-04 04:33 UTC