Today, OpenAI GPT-5.2-Codex is available in Microsoft Foundry and GitHub Copilot. Built for real-world software engineering: sustained reasoning, large-repo awareness, multimodal inputs, and security-aware assistance woven directly into workflows. GPT-5.2-Codex delivers a unified path from in-IDE help to production-grade AI workflows, backed by Azure’s security, compliance, and global scale. This is where powerful models become adoptable. Give it a spin on GitHub Copilot CLI and in VS Code. 🔗 aka.ms/gpt5.2codex
Kevin Foo
2d
This is how “zero to prototype” now works inside real production systems. When intent can be translated directly into repo-aware, security-compliant changes, manual coding stops being the bottleneck. This isn’t code-free software. It’s an evolutionary step where code becomes the medium rather than the interface, and orchestration, judgment, and accountability move to the foreground. Embedding reasoning directly into IDEs, repos, and pipelines is how AI transitions from individual productivity to durable enterprise capability.
Cecil Lacy
1w
AI didn’t stall because it lacked compute. It stalled because it modeled the wrong layer. For decades, artificial intelligence focused on what was easiest to observe: neurons, activations, weights, and shifting topologies. Those are projection effects — not the underlying mechanism. The real mechanism of intelligence lives deeper: in deterministic accumulation, where information is preserved, overlap is exact, history is immutable, and learning occurs by addition rather than mutation. Intelligence is not probabilistic at its core. Probability appears only at the boundary, as novel fragments introduced against an existing substrate. This is the layer modern AI never modeled. The Fragmental Overlap Storage System (FOSS) is my patented implementation of that missing layer — a deterministic, fragmental substrate that accumulates information once, collapses redundancy structurally, and enables adaptive reconstruction without guessing. When intelligence is built on accumulation instead of approximation, systems stabilize. They become auditable. They become reconstructible. They stop relearning what already exists. Once you correct the layer, everything changes.
Cecil Lacy
1w
Over time I’ve noticed a consistent pattern in technical fields: many discussions reach the edge of a conclusion, describe it accurately, and then stop. Drift is identified. Relearning inefficiencies are acknowledged. Auditability and long-lived stability are recognized as requirements. But instead of finishing the logic, the conclusion is softened into “one architectural option among many.” In my experience, that pause isn’t technical, it’s narrative. Finishing the reasoning often collapses comfortable abstractions and forces consequences that people aren’t ready to accept. IMPORTANT: -I didn’t arrive at fragmental systems by reframing the problem. -I arrived there by letting the logic complete. Once accumulation must be immutable and additive, fragmentation and exact overlap aren’t design choices, they’re requirements. Progress doesn’t stall because problems are unsolvable. It stalls because conclusions are interrupted before they’re allowed to finish. — Cecil A. Lacy Inventor of the Fragmental Overlap Storage System (FOSS) & Fragmental Network Protocol (FNP) Patent Pending: 19/264,676
This is the part that actually drives enterprise adoption, not raw model capability, but security, repo awareness, and workflow integration. Models only matter once they’re safe to trust inside regulated production systems.
This is a key inflection point. What really matters here is not just a more capable model, but the fact that GPT-5.2-Codex is being embedded directly into enterprise-grade workflows — with security, compliance and scale by design. This is how AI moves from experimentation to real adoption: when developers trust it inside their daily tools, and organizations can govern it end-to-end. The winners won’t be those with the most advanced models, but those who integrate them responsibly into software engineering, delivery pipelines and operating models.
SHREYAS N
1w
Huge news seeing this finally hit General Availability in Copilot yesterday. The 'Context Compaction' feature is the real game-changer here—finally making long-horizon refactors in large repos viable without the model 'forgetting' architecture halfway through. That 64% on Terminal-Bench 2.0 isn't just a vanity metric; it’s a massive leap for agentic workflows.
Seeing AI go from helper to workflow partner is impressive. Repo awareness plus multimodal inputs changes how engineers think about problem solving, not just code completion.
GPT-5.2-Codex is now live in Microsoft Foundry and GitHub Copilot. Built for real-world software engineering and production workflows, backed by Azure security and scale. Available today in GitHub Copilot CLI and VS Code.
Chander D.
1w
“Unified path from in-IDE help to production-grade AI workflows, backed by Azure’s security, compliance, and global scale.”, Superb!
GPT‑5.2‑Codex woven into Foundry and Copilot shows how reasoning and security can scale together. I think this is a milestone that makes advanced models feel less like research demos and more like everyday engineering partners.