u/ElectricalPilot2297

Most agent demos I see end in a text answer, but a lot of my work ends in a deck. I’ve been testing a workflow where the agent starts with some context, figures out what still needs to be searched or researched, pulls the useful parts together, and then turns the result into a PowerPoint file.

The interesting part is not really AI makes slides, it’s whether the agent can decide what information is missing, what to ignore, and how to structure the output so it doesn’t feel like generic AI filler.

Still needs review, obviously. But it feels more useful than just getting a long summary back. Anyone building something similar?

I’ve been trying a few AI slide tools lately and I’m kind of torn. Most of them are impressive at first. You type in a prompt, it gives you a deck, the slides look clean enough, and it feels like magic for a minute.

Then you actually read the deck. A lot of it is just nicely formatted filler. The kind of stuff that sounds fine until you imagine saying it out loud in a meeting.

The only part that’s been useful for me is using AI before making the slides. Like, not asking it to make a presentation, but asking it to help figure out what the topic should cover, what the argument is, and what the audience would probably care about. Then the slides are easier to make or edit.

I still wouldn’t trust the facts without checking them, and I still end up rewriting a lot. But it does help with the blank-page problem.

Is anyone here actually using AI for real presentations, or is it still mostly demo material?

I’ve been testing an AI presentation tool, and I wanted to share something I found interesting.

Most AI presentation tools I’ve tried are good at turning a prompt into slides, but the content often feels shallow.

But this tool I tested is different. Before generating the presentation, it tries to do deeper research and reason through the topic first. For example, instead of just creating slides like overview / benefits / challenges / conclusion, it tries to think through the whole thing.

Also, the slides feel less like basic templates and more like a polished deck, especially for cover slides, section dividers, and concept visuals.

My takeaway is that AI presentation tools are becoming more useful when they combine research + reasoning + visual generation, instead of only focusing on slide templates.

I’ve been playing around with a small OpenClaw setup for turning local files or context into slides.

Normally when I have a messy meeting recap or project update, I’ll ask an AI tool to summarize it or give me a slide outline. That part is easy enough. The annoying part is still turning that outline into an actual PowerPoint file. So I tried doing the whole thing inside OpenClaw instead.

For the slide part, I used an OpenClaw skill. It runs inside the OpenClaw terminal so I didn’t have to keep copying content back and forth.

The first thing I tried was a project update deck from local notes. I really liked the agent already had the context from the notes, so the slide generation didn’t feel like starting over from scratch.

The output still needed cleanup, especially around slide titles and how much text ended up on each slide. But I’d rather edit a rough deck than manually copy an outline into PowerPoint and rebuild everything slide by slide.

I’ve been playing around with a small OpenClaw setup for turning local files or context into slides.

Normally when I have a messy meeting recap or project update, I’ll ask an AI tool to summarize it or give me a slide outline. That part is easy enough. The annoying part is still turning that outline into an actual PowerPoint file. So I tried doing the whole thing inside OpenClaw instead.

For the slide part, I used an OpenClaw skill. It runs inside the OpenClaw terminal so I didn’t have to keep copying content back and forth.

The first thing I tried was a project update deck from local notes. I really liked the agent already had the context from the notes, so the slide generation didn’t feel like starting over from scratch.

The output still needed cleanup, especially around slide titles and how much text ended up on each slide. But I’d rather edit a rough deck than manually copy an outline into PowerPoint and rebuild everything slide by slide.

I’ve been playing around with a small OpenClaw setup for turning local files or context into slides.

Normally when I have a messy meeting recap or project update, I’ll ask an AI tool to summarize it or give me a slide outline. That part is easy enough. The annoying part is still turning that outline into an actual PowerPoint file. So I tried doing the whole thing inside OpenClaw instead.

For the slide part, I used an OpenClaw skill. It runs inside the OpenClaw terminal so I didn’t have to keep copying content back and forth.

The first thing I tried was a project update deck from local notes. I really liked the agent already had the context from the notes, so the slide generation didn’t feel like starting over from scratch.

The output still needed cleanup, especially around slide titles and how much text ended up on each slide. But I’d rather edit a rough deck than manually copy an outline into PowerPoint and rebuild everything slide by slide.

I’ve been playing around with a small OpenClaw setup for turning local files or context into slides.

Normally when I have a messy meeting recap or project update, I’ll ask an AI tool to summarize it or give me a slide outline. That part is easy enough. The annoying part is still turning that outline into an actual PowerPoint file. So I tried doing the whole thing inside OpenClaw instead.

For the slide part, I used an OpenClaw skill. It runs inside the OpenClaw terminal so I didn’t have to keep copying content back and forth.

The first thing I tried was a project update deck from local notes. I really liked the agent already had the context from the notes, so the slide generation didn’t feel like starting over from scratch.

The output still needed cleanup, especially around slide titles and how much text ended up on each slide. But I’d rather edit a rough deck than manually copy an outline into PowerPoint and rebuild everything slide by slide.

I’ve been playing around with a small OpenClaw setup for a pretty boring work task: turning meeting notes into slides.

Normally when I have a messy meeting recap or project update, I’ll ask an AI tool to summarize it or give me a slide outline. That part is easy enough. The annoying part is still turning that outline into an actual PowerPoint file.

So I tried doing the whole thing inside OpenClaw instead.

For the slide part, I used PopAi’s OpenClaw plugin. It runs inside the OpenClaw terminal / supported agent interface, so I didn’t have to keep copying content back and forth between an AI chat and PowerPoint.

The first thing I tried was a project update deck from messy notes. Nothing fancy: progress, blockers, next steps, decisions needed. That was probably the use case where it made the most sense.

What I liked was that the agent already had the context from the notes, so the slide generation didn’t feel like starting over from scratch. It could keep the same structure and turn it into something closer to a deck.

It was also useful for a short research summary, where I just wanted 5–6 slides to explain the main points.

The output still needed cleanup, especially around slide titles and how much text ended up on each slide. But I’d rather edit a rough deck than manually copy an outline into PowerPoint and rebuild everything slide by slide.

Been trying to improve turning messy information into PowerPoint slides. Usually, the process looks like this:

notes / report / meeting recap
→ ask AI to summarize it
→ ask AI for a slide outline
→ copy the outline into PowerPoint
→ manually split everything into slides
→ clean up the flow

The part that feels most inefficient is the middle step: AI gives a decent structure, but I still have to rebuild the whole presentation manually.

A workflow that’s been more useful for me is generating PowerPoint slides directly from the agent context.

For example, if the agent is already working with:

• meeting notes
• a project update
• a research summary
• a report
• a product idea
• messy bullet points

…it can use that same context to generate a slide deck, instead of only giving a text outline.

I tried this with an OpenClaw plugin that connects the agent workflow to PowerPoint slide generation, and the main benefit was simple: fewer handoffs.

What I liked:

• less copy-paste between AI output and PowerPoint
• easier to turn messy notes into slide structure
• useful when the agent already understands the context
• good for meeting recaps, project updates, research summaries, and pitch-style decks
• makes slide generation feel like part of the AI workflow instead of a separate step

The biggest tip: don’t just ask for “make slides.” Give the AI the audience, goal, and slide count. That usually produces a much better structure.

One thing I’ve noticed with AI agents is that they’re great at helping you think, summarize, and structure information. But a lot of workflows still end as text. That’s fine for some tasks, but for work stuff, I often need an actual deliverable.

I recently came across an OpenClaw plugin, which lets you generate professional decks from inside OpenClaw terminals or OpenClaw-supported agent interfaces.

Instead of just getting a slide outline, the agent can help structure the content and then generate a real deck.

It still needs human review, obviously, but as a first-draft generator, it saves a lot of annoying copy-paste work.

Would be curious to hear whether people prefer this kind of agent-to-artifact workflow, or if they’d rather keep presentations in dedicated visual tools.

[ Removed by Reddit on account of violating the content policy. ]

I used to think AI-generated PowerPoint slides were mostly a gimmick. A lot of examples looked too generic, too polished in the wrong way, or not specific enough to be actually useful. But after trying an OpenClaw plugin that generates PowerPoint slides, I’ve changed my mind a bit.

What made it more useful than I expected was the context. Inside an OpenClaw workflow, the agent is already working with the notes, summaries, goals, and structure behind the presentation. So instead of just giving me a text outline, the plugin can turn that into an actual slide draft.

I see them more as a first draft that saves time on the most annoying part: getting from raw material to an editable deck.

Curious how others here see it. Are OpenClaw plugins for generating PowerPoint slides actually useful, or is this still mostly AI tool hype?

One thing I’ve noticed with AI agents is that they’re great at helping you think, summarize, and structure information. But a lot of workflows still end as text. That’s fine for some tasks, but for work stuff, I often need an actual deliverable.

I recently came across an OpenClaw plugin, which lets you generate professional decks from inside OpenClaw terminals or OpenClaw-supported agent interfaces.

Instead of just getting a slide outline, the agent can help structure the content and then generate a real deck.

It still needs human review, obviously, but as a first-draft generator, it saves a lot of annoying copy-paste work.

Would be curious to hear whether people prefer this kind of agent-to-artifact workflow, or if they’d rather keep presentations in dedicated visual tools.

I still do a lot of spreadsheet work manually, but I’ve been noticing a workflow shift lately.

For simple formulas, writing them by hand is usually still faster. But for the annoying middle zone like longer formulas, multi-condition lookups, repetitive cleanup, grouping, subtotals I’m finding that the real pain often isn’t the logic itself. It’s the syntax and setup.

Instead of manually building a messy formula, I can describe the logic in natural language, get a first draft, and then verify it. Same with some of the repetitive spreadsheet setup work.

The useful part for me isn’t that it replaces spreadsheet skill. It doesn’t.
I still need to know what the formula should do, what bad output looks like, and what needs to be checked manually.

So my current take is AI is actually pretty good at automating the spreadsheet expression layer, but not the judgment layer.

One thing I’ve been noticing lately is that AI is changing where the skill sits in spreadsheet work.

I still write formulas manually a lot, and for simple stuff that’s usually fastest. But once it gets into the annoying middle zone like multi-condition lookups, longer formulas, repetitive cleanup, subtotals, grouping, I’m finding that the real challenge often isn’t the syntax anymore. It’s defining the logic clearly.

That shift stood out to me when I caught myself looking at a messy Excel formula and thinking: I already know exactly what this needs to do. What I don’t want to spend time on is manually assembling and debugging the syntax if an AI tool can generate a workable first pass.

What’s interesting to me is that this doesn’t feel like AI replacing spreadsheet skill. It feels more like the skill is moving.

So now I’m wondering whether this is a broader pattern with AI.

Curious whether other people here are seeing the same thing in their own work, whether it’s spreadsheets, coding, writing, or something else.

One spreadsheet workflow that’s been saving me time lately.

If I already know what the formula should do, but I don’t feel like building or debugging the syntax manually, I’ll describe the logic in plain English first and use AI to generate the formula draft.

That’s been most useful for the annoying middle zone, just the stuff that takes longer than it should because the syntax is tedious.

For example, instead of manually building something ugly like a multi-condition lookup with error handling, I’ll describe directly like “Return the value from column B when column A matches D2 and column C matches E2.”

I’ve been trying this in AI tools, and the useful part for me is that it reads the sheet context first instead of just generating a generic formula.

One place AI has been more useful for me than I expected is internal reporting.

I tried it recently on a sales dataset with multiple regions and several months of data. Instead of starting from scratch, I used AI tools for the first pass of the reporting workflow.

What helped was getting through the repetitive reporting setup faster, like supporting calculations, summary structure, and a draft I could actually review instead of building every piece manually.

I still checked the numbers and rewrote parts of the output, so I wouldn’t describe it as fully automated. But for the part of business analysis that involves repetitive spreadsheet scaffolding, it was a pretty solid time saver.

Interested in where other people are actually finding ROI from AI in reporting/ops/analytics workflows.

I didn’t expect this to be one of the more useful AI use cases for me, but quick spreadsheet charts have turned out to be a pretty good one. Not because the charts are magically perfect. They aren’t.

I tried this recently in AI spreadsheets tool on a sales sheet and asked for a few basic charts. The first pass wasn’t final output quality, but it was close enough that editing was easier than starting from scratch. That’s been enough to change my behavior a bit. I’m a lot more likely now to try a quick chart just to see whether there’s a pattern worth following up on.

So for me, AI doesn't make better charts, it just makes low-stakes exploration feel cheap enough to do more often.

Curious if other people have noticed the same thing.