Codex does more when you tell it what tools it has.

Most people use Codex like a chatbot. It's actually closer to an agent — if you configure it right.

Plugins connect Codex to real data sources and APIs. Skills turn your repeatable workflows into single commands. Together they let Codex act, not just answer.

Use the [tool name] plugin to pull current data, then apply the [skill name] workflow to format the output.

Set it up once, reuse it forever )

Codex does more than write code — if you configure it right.

Most people open Codex and just start typing. The settings panel gets ignored entirely.

Codex lets you set persistent personalization — preferred stack, response detail level, and task permissions — so it stops asking obvious clarifying questions and starts running autonomously on your workflow.

My stack: TypeScript, Node, Postgres. Skip setup questions. Default to production-ready code with error handling.

Set it once, stop repeating yourself every session )

ChatGPT can now run tasks in the background while you do other things.

Workspace agents are Codex-powered — meaning they don't just generate text, they actually execute multi-step workflows in the cloud.

The key shift: these agents operate on your tools and files autonomously, not just inside the chat window. You assign a task, they run it, you review the result.

Think code reviews, data pulls, scheduled reports — handed off completely.

Try assigning one repetitive team task this week and see how far it gets )

ChatGPT for Teams can now run tasks while you're not watching.

Business and Enterprise users get custom workspace agents — bots you build once that run on their own.

The practical shift: instead of prompting ChatGPT each time, you configure an agent with a goal and tools (Slack, Gmail, web search) and it handles recurring work automatically. Think "monitor product feedback → summarize → post to Slack" as a single persistent agent.

If you're on a paid team plan, worth building one this week for any task you repeat more than twice.

Start simple )

Recently I caught myself doing something weird.

I wrote a Slack message. Then re-wrote it three times to sound less like AI.

Not because the AI version was wrong. Because the AI version wasn't me.

That's a strange new editing pass. "Make this more obviously written by a human."

Anyone else?

OpenAI is pushing Codex into enterprise dev teams at scale.

4M weekly active users. Now they're adding Accenture, PwC, and Infosys as deployment partners.

The real play here isn't the tool — it's the system integrators. These firms know how to embed software into large org workflows, compliance requirements, and existing codebases. That's the hard part Codex alone can't solve.

If your company is slow-walking AI in dev, your integrator partners are about to start asking questions.

Worth watching how fast this moves from "pilot" to "standard stack" )

Google just put an AI intern inside every doc, sheet, and email.

Workspace Intelligence is now baked into the full Google suite — not a chatbot on the side, but automated actions triggered inside your existing workflow.

The shift is from "ask AI" to "AI already did it." Workspace Intelligence runs in the background — summarizing threads, drafting replies, prepping meeting notes — before you even open the file.

Less context-switching, more work already handled when you arrive.

Set up auto-summaries on your busiest Gmail labels first — that's where the time savings hit hardest )

Typing is often the slowest part of your workflow.

Most people try voice-to-text once, get burned by bad accuracy, and never go back. The tools got better — you probably didn't notice.

The gap between dictation apps now comes down to one thing: post-processing. The best tools don't just transcribe — they auto-punctuate, remove filler words, and reformat your speech into clean text before you even see it.

Test any app with this to see how it handles cleanup:

Um, so basically… I need to, uh, schedule a meeting — yeah — for next Tuesday if that works.

If it outputs that verbatim, find a different app )

AI headshot tools want a workflow you don't have.

A dozen selfies. A day of training. A monthly subscription. Hope the face still looks like you.

This one wants 1–3 selfies and runs inside Telegram.

You get:
- One finished portrait
- Three Smart Templates
- Same chat, no upload form

Generic image gen builds a polished plastic stranger. Two regenerations and the identity drifts.

Tap below, send a selfie )

Claude now controls your Spotify, Uber Eats, and TurboTax.

Not just answering questions about them — actually operating inside them.

Anthropic expanded Claude's app connectors to personal life: order food, find hiking trails, file taxes, queue music — all from one conversation. The AI suggests relevant apps mid-chat based on what you're asking.

If you have Claude, go to settings and connect one app you use daily. Then ask it something you'd normally open that app for.

See how far it gets )

You can now leave AI to do its job – and walk away

In the past, AI would only respond when you asked it a question. Now, most large models have scheduled background tasks.

This isn’t automation for programmers – it’s literally ‘set a timer and forget about it’. The model will start up on its own, complete the task and send you the result.

Three scenarios that actually work:

Every morning — a brief digest of news on your topic.
Once a week — an analysis of expenses from a spreadsheet.
On a schedule — a draft weekly report, ready for editing.

Every Monday at 9am, summarise my notes from last week and list 3 priorities for this week

Try setting up at least one task like this )

OpenAI + Infosys: enterprise AI just got a big distribution channel.

Infosys has 300+ enterprise clients. Now they're pushing OpenAI tools directly into those workflows.

The focus isn't flashy demos — it's three unglamorous but high-value areas: modernizing legacy code, automating DevOps, and rebuilding software development pipelines. That's where the real enterprise money gets spent.

If your company uses any large IT consultancy, AI tooling may arrive through them before your internal team ever evaluates it.

Worth tracking if you work in enterprise software )

Hyatt rolled out ChatGPT Enterprise to its entire global workforce.

Not a pilot. Not one department. The whole company.

The real move here: pairing GPT-4o with Codex so non-technical staff can automate repetitive workflows without writing actual code. Productivity gains come fastest when you remove the "I'm not a developer" bottleneck.

If your team still treats AI as an individual tool rather than shared infrastructure, you're leaving the biggest gains on the table.

Start with one repetitive team process this week — draft a prompt for it )

OpenAI quietly swapped the default ChatGPT model.

GPT-5.5 Instant is now what you get when you open ChatGPT. No announcement fanfare, just a new default.

The main improvement is fewer hallucinations in high-stakes domains — law, medicine, finance — without sacrificing response speed. That's the tradeoff that usually breaks these models: accuracy or latency. This one holds both.

If you use ChatGPT for research or professional drafts, test it on something where errors actually matter.

Worth verifying yourself )

PwC is rebuilding CFO workflows with OpenAI agents.

Finance teams spend massive time on reporting, reconciliation, and forecasting. Most of it is repetitive enough to automate.

The partnership deploys AI agents across the full finance function — not just one task. Forecasting, controls, compliance reporting — all connected, so the CFO office stops being a bottleneck and starts being a real-time signal.

If you're in finance ops, start small: pick one recurring report and prompt an agent to draft it weekly.

Summarize this month's variance report, flag any line items outside ±5% of forecast, and suggest 3 possible causes.

Good starting template for any finance team )

One dev firm cut build time significantly — here's the actual workflow.

Simplex integrated ChatGPT Enterprise and Codex across the full dev cycle — not just coding, but design and testing too.

The real shift wasn't using AI to write code. It was plugging Codex into every phase — so bottlenecks in design review and QA shrank alongside build time, not separately.

Try scoping your next feature with AI before touching the editor:

Break this feature into design decisions, implementation steps, and edge cases to test. Flag anything ambiguous before I start building.

Front-loading clarity saves more time than fast code generation )

AI теперь можно оставить работать — и уйти

Раньше ИИ отвечал только когда ты спрашивал. Теперь у большинства крупных моделей появились фоновые задачи по расписанию.

Это не автоматизация для программистов — это буквально «поставь на таймер и забудь». Модель сама запустится, сделает задачу и пришлёт результат.

Три сценария, которые реально работают:

Каждое утро — краткий дайджест новостей по твоей теме.
Раз в неделю — анализ расходов из таблицы.
По расписанию — черновик еженедельного отчёта, готовый к правке.

Every Monday at 9am, summarize my notes from last week and list 3 priorities for this week

Попробуй настроить хотя бы одну такую задачу )

Most AI support bots sound like bots. Parloa is fixing that.

Enterprise customer service runs on hold music and scripted menus. Nobody wants to call in.

Parloa builds voice agents on top of OpenAI models with a simulate-before-deploy approach — companies test thousands of synthetic conversations before going live, so the agent handles edge cases instead of breaking on them.

Worth applying the same logic to your own AI workflows — simulate failure cases before shipping.

Works every time )

OpenAI's new voice models can now reason mid-conversation.

Not just transcribe — actually think while you talk. Big difference.

The new realtime API models combine speech understanding with reasoning, so the voice layer handles translation, transcription, and logic in one pass — no text-conversion detour required.

If you're building voice features, skip the speech-to-text → LLM → TTS chain.

Try the realtime API directly )