SYSTEMS - AI Dynamics

Continual Learning Benchmark: Knowledge Transfer for AI Models

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16 February 2026 8h33

Thanks, still finishing the blog post so I'll cover all that! It's designed closer to a continual learning benchmark, new context for each problem and models get to transfer their lessons to future selves. They also have access to prior solutions, as that handoff is necessary to

→ View original post on X — @alexjc

16 February 2026

Agentic AI: From Tools to Systems Thinking

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@ingliguori

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15 February 2026 14h17

AI-ready ≠ tool-ready. This cheatsheet shows the real shift:
Models → Systems
Prompts → Planning
Outputs → Outcomes Agentic AI rewards systems thinkers — not tool collectors.

→ View original post on X — @ingliguori

15 February 2026

Blev Labs’ new cognitive architecture outperforms OpenClaw, offers reports

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@scobleizer

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15 February 2026 10h11

This wasn't done with OpenClaw though. It was done with a new kind of AI: cognitive architecture by @blevlabs
. It's way better than what everyone is using. But you are right. And I can get you a report like this on any topic.

→ View original post on X — @scobleizer

15 February 2026

Designing Machine Learning Systems for Production

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@kirkdborne

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14 February 2026 20h55

Designing #MachineLearning Systems — An Iterative Process for Production-Ready Applications: http://
amzn.to/46epLSi by @chipro — 𝓣𝓸𝓹𝓲𝓬𝓼:
Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing,

→ View original post on X — @kirkdborne

14 February 2026

Les 4 boucles fondamentales de l’IA autonome

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@ingliguori

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14 February 2026 19h25

Agentic AI isn’t about learning 10 steps. It’s about mastering 4 loops:
Perception → Memory → Planning → Action. Frameworks change.
Autonomy principles don’t. Build agents that think in systems, not prompts.

→ View original post on X — @ingliguori

14 February 2026

Four Major AI Trends for 2026: 3D, Robotics, Agentic Orchestration

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@dfintelligence

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14 February 2026 10h33

There are four major trends that I see for 2026 in AI: – 3D generation – Robotics, particularly data-to-actions systems (VLM, LAM, world models) – Agentic management and orchestration (and I mean real orchestrations, not bullshit stuff from PDF sellers with n8n) – The

→ View original post on X — @dfintelligence

14 February 2026

Architecting AI Systems: Model Federation and Pixel-to-Policy Approaches

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@bilawalsidhu

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13 February 2026 23h14

it continues to be the most compelling analogy for me — federation of task specific models and classical systems to just pixels in policy out

→ View original post on X — @bilawalsidhu

13 February 2026

Error Recovery Rate Gap in AI Task Performance

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@snorkelai

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13 February 2026 22h33

Passed and failed tasks encounter similar errors (2.09 vs 2.71 per task). The difference? • Passed tasks recover from 95.0% of errors • Failed tasks recover from 73.5% A 21.5-point gap. Recovery > avoidance.

→ View original post on X — @snorkelai

13 February 2026

Enterprise AI Requires Sovereign Hybrid Cloud Technology Stacks

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@officialindiaai

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13 February 2026 17h49

Sandip Patel, MD, IBM India South Asia, highlights a critical reality. He highlights that AI success requires more than powerful models and that Enterprises need strong, trusted and sovereign technology stacks built on hybrid cloud foundations. These must enable model choice,… pic.twitter.com/sRAGXYhXKr
— IndiaAI (@OfficialINDIAai) 13 février 2026

Sandip Patel, MD, IBM India South Asia, highlights a critical reality. He highlights that AI success requires more than powerful models and that Enterprises need strong, trusted and sovereign technology stacks built on hybrid cloud foundations. These must enable model choice,

→ View original post on X — @officialindiaai

13 February 2026