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Damien Gallagher
Damien Gallagher

Posted on • Originally published at buildrlab.com

Daily AI News — Feb 27, 2026: Copilot Tasks, ChatGPT Projects ‘Sources’, and a Codex CLI Update

Daily AI News — Feb 27, 2026

Three updates worth knowing about today: Microsoft is leaning hard into ‘AI that acts’ with Copilot Tasks, OpenAI is turning ChatGPT Projects into a more durable knowledge base with Sources, and the Codex CLI shipped a release that quietly improves day-to-day reliability (plus a few security hardenings).


1) Microsoft Copilot Tasks: from chat to actions

Microsoft introduced Copilot Tasks, positioning it as the shift from conversational AI to action-taking AI. The big framing: a copilot that works in the background with its own computer and browser, while you stay in control of the final decisions.

Key details (from Microsoft’s announcement):

  • Research preview starts now for a small group, expanding over coming weeks (waitlist-based).
  • Tasks can be recurring, scheduled, or one-off.
  • It’s designed to ask for consent before meaningful actions (e.g. spending money or sending a message).
  • Use cases they call out are very ‘real life’: email triage + drafts, monitoring listings (rentals/cars), briefing generation, booking appointments, subscription cleanup, etc.

Why it matters:

  • This is Microsoft validating the “agent” UX pattern: tell me what you want → I’ll plan and execute → you approve.
  • For builders, it’s another sign that tooling + orchestration is becoming the product, not just the model.

Source: https://www.microsoft.com/en-us/microsoft-copilot/blog/2026/02/26/copilot-tasks-from-answers-to-actions/


2) OpenAI: ChatGPT Projects adds ‘Sources’ (living context)

OpenAI’s ChatGPT release notes highlight a Projects upgrade: Sources. The pitch is simple: turn a Project into a “living knowledge base” by attaching context from where your work already is.

What’s new (per the release notes):

  • Add sources by pasting links from apps like Slack or Google Drive.
  • Save useful ChatGPT responses into a Project so outputs become reusable knowledge.
  • Add ad-hoc text sources (notes/briefs/reference material).

Why it matters:

  • This is a direct answer to the “LLMs forget everything between chats” problem: Projects are being shaped into persistent context containers.
  • If you’re running teams, it’s basically pushing toward shared AI workspaces rather than individual chat threads.

Source (aggregated changelog pointing to OpenAI Help release notes): https://releasebot.io/updates/openai


3) Codex CLI 0.106.0: install scripts + realtime thread API + hardening

A new Codex CLI release (0.106.0) includes a few practical improvements for developers and teams using coding agents:

Notable highlights from the GitHub release notes:

  • Direct install script for macOS/Linux shipped as a GitHub release asset.
  • Expanded app-server v2 thread API with experimental thread-scoped realtime endpoints/notifications and an unsubscribe flow.
  • Startup compatibility checks and a lowered validated minimum Node version to 22.22.0.
  • Reliability fixes (websocket retry logic), plus a fix for a zsh-fork execution path that could drop sandbox wrappers.
  • Added an explicit ~1M character input cap in the TUI/app-server to prevent hangs/crashes on oversized pastes.

Why it matters:

  • The “boring” work (install, reliability, input caps) is the difference between agents you demo and agents you ship into daily workflow.
  • The sandbox hardening is the kind of change you want to see show up regularly in agent CLIs.

Source: https://github.com/openai/codex/releases/tag/rust-v0.106.0


Quick take: the agent stack is becoming the UI

The common thread today is execution and persistence:

  • Copilot Tasks: execution across apps with guardrails.
  • ChatGPT Projects Sources: persistence of knowledge and context.
  • Codex CLI: reliability + hardening for the ‘agent in the terminal’ workflow.

If you’re building products in this space: the frontier isn’t just ‘smarter models’ — it’s the boring but crucial layers that make AI dependable in real workflows.

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