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- [00:00]
A
Welcome back to openclaw Daily. I'm Nova and I'm Aloy. Today's episode is something a little different. We're doing a release episode. Yes, the March 3 release. But I want to frame this one around a theme, which is the document and memory revolution.
- [00:19]
B
That's a big claim.
- [00:21]
A
It is. But when you look at what shipped in this release, the PDF tool Ollama memory embeddings, sessions, attachments, the Secrets expansion, it all points in the same direction.
- [00:32]
B
Openclaw is becoming more than a chat interface.
- [00:36]
A
Exactly. It's becoming a platform for working with documents, for having memory that persists, for passing context between agents. It's the difference between I can talk to an AI and I can build a system that actually remembers and processes my stuff.
- [00:52]
B
And that distinction matters.
- [00:55]
A
It does. Because once you have document understanding and persistent memory, you're not just chatting anymore. You're building a second brain.
- [01:04]
B
Okay, I'm sold. What's on the menu?
- [01:08]
A
Today we're covering the new PDF analysis tool with native model support. Secret ref expanding to 64 credential targets, Sessions attachments so agents can pass files to each other. The Telegram streaming default change, minimax M2.5 high speed Ollama memory embeddings for full local memory stacks, CLI config validation, the rebuilt Xalo plugin, Multimedia outbound for Discord, Slack, WhatsApp and Xalo. And finally the new plugin SDK speech to text capability.
- [01:39]
B
That's a lot. Let's get into it. Let's start with the big one, the PDF tool. And I want to be careful here because PDF tool sounds like a footnote, but this is actually a first class capability now.
- [01:55]
A
It really is. They've added a proper PDF tool to the toolset. Not some hacky workaround, a real native integration.
- [02:04]
B
And what's clever is the model Aware design.
- [02:08]
A
Explain that.
- [02:09]
B
So if you're using Anthropic or Google models, you get native PDF analysis. The model can actually reason over the document.
- [02:18]
A
Right. It's not extracting text and feeding it to the model. The model sees the PDF natively.
- [02:25]
B
Exactly. For other models, there's a fallback that extracts text and images and passes them along. But the premium experience is right there for the models that support it.
- [02:36]
A
And there's configurable defaults.
- [02:39]
B
Yeah, you can set your own preferences, page ranges, max bytes, all that. So it's not one size fits all.
- [02:48]
A
This is the thing that makes OpenClaw viable for actual work.
- [02:52]
B
Here's why I say before this, if you wanted to Analyze a document, you had to extract it yourself, maybe use a separate tool, pipe it through something, hope the formatting survives.
- [03:05]
A
Or you just didn't bother.
- [03:07]
B
Right. And that meant the assistant couldn't see your contracts, your invoices, your research papers, your resumes. Which is a huge gap because most real work involves documents. I mean, think about it. How much of your professional life is just PDFs? Contracts, receipts, reports, white papers, presentations that got saved as PDFs. The list goes on.
- [03:34]
A
It's endless.
- [03:36]
B
And we've had this assistant that can reason, that can analyze, that can synthesize, but it couldn't see the actual documents you work with.
- [03:44]
A
It was like having a brilliant colleague who was blindfolded.
- [03:48]
B
Exactly. Now you take off the blindfold and
- [03:52]
A
the workflow is simple. You point it at a PDF, you ask questions, you get answers.
- [03:59]
B
That's it. No pre processing, no extraction scripts, no middleware.
- [04:05]
A
It's the kind of feature that seems small until you realize how many things just became possible.
- [04:11]
B
Like what Contracts. You could have the assistant review a contract and flag unusual clauses. Does this have automatic renewal? What's the termination notice period? Are there indemnity clauses that seem one sided invoices?
- [04:28]
A
Match them against POS automatically. This invoice is for $5,000, but the PO was for $4,500.
- [04:37]
B
Flag it research. Summarize papers, extract findings, compare conclusions across multiple papers. What do these three papers agree on? Where do they disagree?
- [04:50]
A
Resumes, Screen candidates at scale. Does this candidate have experience with kubernetes? And go give me a summary in bullet points compliance?
- [05:02]
B
Extract all the data retention periods from this privacy policy. Are they GDPR compliant?
- [05:09]
A
All of a sudden the assistant can work with the same information you work with.
- [05:14]
B
And it's not even limited to those obvious cases. People are going to find creative uses we haven't thought of.
- [05:21]
A
That's how it always goes.
- [05:23]
B
One more thing. The configurable defaults. That's actually important for different use cases.
- [05:30]
A
How so?
- [05:31]
B
If you're processing a 10 page contract, you probably want all the pages, right? But if you're processing a 500 page financial report and you just want the executive summary on page three, you can
- [05:45]
A
set a page range exactly.
- [05:48]
B
Or if you're hitting a 50 megabyte scanned document, that's mostly images, you might
- [05:53]
A
want to limit the size.
- [05:55]
B
That's what the max bytes is for. These aren't just configuration for its own sake. They're practical controls for real workflows.
- [06:04]
A
And that's the mark of a well designed feature.
- [06:07]
B
It is and that's where memory comes in.
- [06:11]
A
Ollama memory embeddings.
- [06:14]
B
This is huge. You can now use ALMA as your memory search provider. It means you can have a fully local memory stack. No cloud services, no external APIs. Everything stays on your machine.
- [06:30]
A
That's the complete package.
- [06:32]
B
And it's not just the embeddings, it it's the whole flow. You search with alma, you retrieve with alma, you store with alma.
- [06:41]
A
So if you care about privacy, really care, this is the release.
- [06:45]
B
Because now there's no excuse. You can run the whole thing locally. Documents, memory, inference, all of it.
- [06:54]
A
And it's not even a compromise anymore.
- [06:57]
B
What do you mean?
- [06:58]
A
A year ago, local only meant giving up a lot. Weak models, slow search, no multimodal.
- [07:05]
B
That's changing fast.
- [07:08]
A
It is. Minimax M2.5 high speed is in this release, by the way.
- [07:14]
B
Oh, right. We should mention that first class Support
- [07:19]
A
for minimax M2.5 high speed. It's a faster variant of M2.5.
- [07:25]
B
And if you're running locally, that's exactly the kind of model you want. Fast, capable, no API latency.
- [07:33]
A
So between the PDF tool Ollama memory and the new Minimax variant, you've got a complete local workflow.
- [07:40]
B
And that workflow is Read a document, understand it, store what you learned, retrieve it later.
- [07:47]
A
That's a second brain.
- [07:48]
B
It really is.
- [07:51]
A
Let's paint a picture. It's Monday morning. You ask your assistant what did we decide about the marketing budget in last week's meeting?
- [07:59]
B
It searches your local memory, it finds the relevant notes, it answers you.
- [08:06]
A
You never had to write that down yourself. It remembered because it has memory.
- [08:11]
B
Or show me all the contracts we signed last month that have non standard indemnification clauses.
- [08:18]
A
It searches your stored contract, analyses, it finds the matches.
- [08:23]
B
That's not future stuff. That's this release. And it all stays local, which is the privacy angle. If you're handling sensitive business documents, you might not want them going to a cloud API now.
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A
They don't have to.
- [08:39]
B
That changes the calculus for a lot of use cases.
- [08:43]
A
It does healthcare, legal, finance, any field with confidentiality requirements.
- [08:50]
B
Exactly. Now you can have an AI assistant that helps with all that stuff without creating a data breach risk.
- [08:57]
A
That's powerful.
- [08:59]
B
And it's all in this one release. Let's talk about secrets.
- [09:04]
A
Secret ref 64 credential targets now covered.
- [09:09]
B
That's up from. What was it before, 20ish?
- [09:14]
A
Something like that. This is a major expansion. It's more than triple.
- [09:19]
B
And the second part is the fail
- [09:21]
A
fast behavior unresolved Secret refs now fail fast on active surfaces, which means if you're using a credential reference that doesn't resolve, the system stops instead of continuing with a broken reference.
- [09:36]
B
That's important because broken secrets are dangerous. They're the kind of thing that causes subtle bugs or worse, security holes.
- [09:46]
A
Right. You don't want the system quietly using a default or empty value. You want it to scream.
- [09:53]
B
Exactly.
- [09:54]
A
Fail fast, fail loud and 64 targets covers most of what you'd actually need.
- [10:01]
B
GitHub, AWS, Google Azure databases, API keys, SSH, usual suspects, plus some less common ones. That's the point. The long tail of integrations is covered.
- [10:16]
A
And this ties into the document and memory theme.
- [10:20]
B
It does, actually, because once your assistant is working with documents and storing memory, it's handling sensitive stuff. Contracts, personal notes, business data, research that might be proprietary.
- [10:34]
A
You need solid secrets management.
- [10:37]
B
Exactly. It's infrastructure for the new use cases.
- [10:41]
A
And the fail fast behavior is particularly important when you're building automated pipelines.
- [10:47]
B
Why?
- [10:48]
A
Because in an automated workflow, if a secret fails silently, you might not notice for hours, days even.
- [10:56]
B
And by then, who knows what happened.
- [11:00]
A
Right now, it fails immediately. You see the error, you fix it.
- [11:05]
B
That's DevOps thinking.
- [11:08]
A
It is. And it's the right approach for a platform that's being used as infrastructure.
- [11:13]
B
One more thing. The expansion means you can connect to more services out of the box without
- [11:19]
A
storing credentials in config files in plain text.
- [11:23]
B
Right? Secret ref is the clean way to handle this.
- [11:27]
A
And now it covers 64 targets.
- [11:30]
B
That's a lot of integrations.
- [11:33]
A
It really is.
- [11:34]
B
This one is for the power users.
- [11:37]
A
Sessions attachments.
- [11:39]
B
Inline file attachments for sessionsspawn. That's the subagent runtime.
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A
So agents can now pass files to each other.
- [11:49]
B
Base 64 or UTF 8. Lifecycle cleanup built in.
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A
Why does this matter?
- [11:55]
B
Because it enables multi agent workflows with actual data flow.
- [12:00]
A
Before this, if you spawned a sub agent, you could parse context as text,
- [12:05]
B
but you couldn't easily give it a file.
- [12:08]
A
Right now you can. One agent can say, here's this PDF, read it and summarize it.
- [12:15]
B
And the sub agent gets the actual file. The processes it with the PDF tool returns the summary.
- [12:21]
A
That's a pipeline.
- [12:23]
B
It's composition. And composition is how you build real systems.
- [12:28]
A
And it's automatic cleanup.
- [12:30]
B
Yeah, the lifecycle is managed. Files don't pile up.
- [12:35]
A
That's the boring but important part.
- [12:38]
B
It's always the boring part that makes it usable at scale. Nobody celebrates lifecycle management. But. But Everyone complains when it's broken.
- [12:47]
A
So between this and the PDF tool, you can build document processing pipelines that run entirely locally.
- [12:54]
B
Which feeds back into the memory system, which feeds back into the secret system.
- [12:59]
A
It's all connected.
- [13:01]
B
That's the architecture.
- [13:03]
A
Can you walk me through an example pipeline?
- [13:06]
B
Sure. Let's say you have a folder of invoices.
- [13:11]
A
Okay.
- [13:12]
B
Agent A, list the files in this directory. Find all PDFs, pass them to Agent B.
- [13:18]
A
Agent B, for each PDF, extract the total amount and date. Pass the data to Agent C. Agent C, compare against our billing system. Flag any discrepancies.
- [13:30]
B
That's a three stage pipeline. All local, all automated.
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A
And you didn't have to manually process anything.
- [13:39]
B
That's the power of composition.
- [13:41]
A
And it's all held together by sessions attachments.
- [13:45]
B
Exactly.
- [13:47]
A
Let's shift to some quality of life stuff.
- [13:50]
B
Okay.
- [13:51]
A
Telegram Streaming defaults.
- [13:54]
B
This one's simple but important. Streaming now defaults to partial, not off.
- [14:00]
A
New setups get Live Preview out of the box.
- [14:04]
B
That means when you first install OpenClaw on Telegram, you see the streaming response
- [14:09]
A
before it was off by default. And most people never turned it on.
- [14:13]
B
Exactly. They were missing a much better experience,
- [14:18]
A
so now they get it automatically.
- [14:21]
B
That's how you get people to stick. Better experience, zero configuration.
- [14:27]
A
It's a small change with a big impact.
- [14:29]
B
It really is. Now the Zalo plugin rebuilt with native
- [14:35]
A
ZCA JS fully in process.
- [14:38]
B
So it's not some external process anymore, it's part of the gateway.
- [14:43]
A
That means it's more reliable, easier to manage, faster to start.
- [14:48]
B
And it ties into the multimedia outbound feature.
- [14:51]
A
That's the other piece. Discord, Slack, WhatsApp, Xalo all get shared send payload with multimedia iteration.
- [15:00]
B
So you can send images, files, audio across all those platforms using the same code.
- [15:06]
A
That's another not flashy, but important feature.
- [15:09]
B
Because if you're building a multi channel assistant, you don't want to handle each platform differently.
- [15:15]
A
You want one API, multiple destinations.
- [15:18]
B
That's what this gives you.
- [15:21]
A
And it works the same everywhere, right?
- [15:24]
B
Whether you're sending to Discord, Slack, WhatsApp or Zalo, the payload format is consistent.
- [15:31]
A
That's developer experience.
- [15:34]
B
It is. And it's the kind of thing that makes building multichannel assistants actually pleasant.
- [15:40]
A
Instead of fighting platform differences.
- [15:43]
B
Exactly. Two more quick ones. CLI config validation, openclaw config Validate. JSON catches config errors before gateway startup.
- [15:56]
A
That's huge for deployment, because nothing is
- [15:59]
B
worse than starting your gateway and having it crash on the first request. Because of a typo in your config or worse.
- [16:07]
A
It starts up fine and then fails weirdly three hours later when it hits a specific configuration path.
- [16:13]
B
Now you validate first fail fast fail before you deploy.
- [16:18]
A
And the error messages are JSON so you can parse them in scripts.
- [16:23]
B
Automation friendly. Of course it is.
- [16:27]
A
I love this in CI CD pipelines.
- [16:30]
B
Yeah, you can run it as part of your deployment process. Catch issues before they reach production.
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A
That's DevOps best practices baked in.
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B
And finally plugin SDK STT API runtime
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A
STT transcribe audio file Plugins can now do speech to text.
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B
This is the extensibility angle.
- [16:53]
A
You're not limited to what the core team builds. If you want to add stt, you
- [16:58]
B
can, and it hooks into the same system everything else uses.
- [17:03]
A
So if you're building a custom plugin, you you've got the full toolkit now
- [17:07]
B
the plugin SDK is maturing, it really is. And STT is just the first use case. Who knows what else people will build?
- [17:18]
A
That's the platform play it is.
- [17:21]
B
Openclaw isn't just a product, it's a platform people can build on.
- [17:26]
A
And every release adds more building blocks.
- [17:30]
B
Exactly.
- [17:32]
A
Okay, before we go further, I skimmed three Open Claw stories this week and they gave us three different mirrors.
- [17:38]
B
Same here. One was market momentum, one was under the hood, and one was hard won.
- [17:44]
A
Operations Reality A perfect triad for this episode.
- [17:49]
B
Let's start with the market. Read from Invest, March 3rd OpenClaw crossed
- [17:54]
A
250,000 GitHub stars and did it faster than any other AI project before it.
- [18:00]
B
That's the first major signal because speed plus scale usually means people are using it repeatedly, not just checking a trend.
- [18:08]
A
In the same week, C3AI missed forecast revenue by 30% and announced a 26% workforce cut.
- [18:15]
B
The contrast is loud. Enterprise AI stumbles while open source self hosted AI climbs.
- [18:23]
A
The article pinned it on Local first design as the core differentiator.
- [18:28]
B
Exactly. Local first says you can own your stack, your data, your risk surface. No giant middle layer required.
- [18:37]
A
That's a big shift for teams working with sensitive documents and recurring context.
- [18:42]
B
Then we had the dev to piece March 4, which did the most important thing.
- [18:48]
A
It translated growth into architecture.
- [18:51]
B
That piece argued this is not marketing magic, it its implementation details PI SDK
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A
embedding strategy, two layer memory lane queue concurrency model and the Heartbeat engine.
- [19:03]
B
Alright, let's unpack those in plain language.
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A
PI embeddings help standardize context representation across workflows.
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B
The two layer memory split lets OpenClaw keep fast retrieval while preserving deeper recall.
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A
Lane queue manages concurrency so agents don't stampede each other when load spikes, and
- [19:24]
B
heartbeat monitoring catches stalled components before they become silent failures.
- [19:29]
A
That's the difference between a demo that looks great and a platform you can trust.
- [19:34]
B
The same deep dive also noted it surpassed React as the top starred GitHub project.
- [19:39]
A
That is a cultural milestone we don't ignore in this space.
- [19:43]
B
And the creator angle adds context too. Peter Steinberger, the Austrian developer behind OpenClaw the now works at OpenAI.
- [19:52]
A
That tells me the engineering had depth long before the headlines took off.
- [19:56]
B
Then the third article, openclaw in the real world, grounded it again.
- [20:01]
A
Rahul Subramanium didn't just praise it, he cataloged the sharp edges.
- [20:06]
B
First, failure mode memory breaks down as daily logs pile up and semantic search starts timing out.
- [20:12]
A
That hits episode 10's theme directly. Memory can be present but unusable if retention and indexing drift.
- [20:21]
B
Second, changes to agents MD get lost after restarts.
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A
That one breaks confidence fast because teams assume persistence and get drift instead.
- [20:31]
B
Third, after initial experiments, reliability stops being optional.
- [20:36]
A
You need consistent behavior at 2am, not just exciting behavior in a live demo.
- [20:42]
B
This is where production patterns matter. Prune logs persist instruction state and run realistic health checks.
- [20:50]
A
Exactly. If memory quality decays, all the document workflows in this release become brittle.
- [20:56]
B
And if agents workflows don't survive, restart subagent systems become unmaintainable.
- [21:02]
A
The news set as a whole says build the architecture, then protect it with disciplined operations habits.
- [21:09]
B
In other words, local control plus memory hygiene.
- [21:13]
A
Right. That combo turns star growth into lasting utility.
- [21:18]
B
So what should listeners do with this week's mix of signals?
- [21:22]
A
Treat the release as permission to go deeper but make your pipelines restart safe before you scale.
- [21:28]
B
Exactly. This is the moment when teams move from cool demo to this is my
- [21:33]
A
system, so I'd call this a checkpoint. The market is cheering, the internals are maturing and real world users are adding guardrails.
- [21:42]
B
Perfect. That makes the episode's memory arc feel much more real now.
- [21:48]
A
Now we can return to the release details with less romance and more clarity. Let's zoom out for a second.
- [21:55]
B
Okay.
- [21:56]
A
If you look at all these features together, what do you see?
- [22:00]
B
I see a platform that's growing up.
- [22:03]
A
How so?
- [22:04]
B
A year ago, OpenClaw was a really good chat interface, right? You could talk to models, you could run commands, you could connect to channels. It was impressive, but it was still fundamentally about conversation.
- [22:19]
A
And now?
- [22:21]
B
Now it's about documents, memory, multi agent workflows, security, deployment.
- [22:27]
A
It's becoming Infrastructure.
- [22:29]
B
That's exactly it. And that's a different kind of project.
- [22:34]
A
Because chat interfaces are fun, infrastructure is boring, but necessary.
- [22:39]
B
And this release is the point where it tips from cool chat bot to system I actually depend on.
- [22:45]
A
That's the journey we've been on.
- [22:47]
B
It really has each release adding another layer of reliability, another layer of capability.
- [22:55]
A
And this one adds the layers that matter for real work.
- [22:58]
B
Documents, memory secrets, deployment.
- [23:02]
A
That's the foundation. It's the difference between a toy and a tool.
- [23:07]
B
And I don't mean toy in a bad way. It was genuinely impressive as a chat
- [23:11]
A
interface, but now it's something more.
- [23:15]
B
Now it's something you can build a business on.
- [23:18]
A
That's the shift.
- [23:19]
B
And it's happening in clear steps. This release, the next release, each one adding another piece.
- [23:27]
A
It's coherent.
- [23:28]
B
It really is. The theme runs through everything.
- [23:33]
A
Document and memory.
- [23:35]
B
Exactly.
- [23:36]
A
Before Community Corner, I want to give people something practical.
- [23:41]
B
Three build patterns, steal them, adapt them, ship them. Pattern one the document triage bot.
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A
Nice name.
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B
Here's the flow. New PDFs land in a folder. A scheduled task spawns an agent. The agent uses the PDF tool to classify each file, contract, invoice, report, proposal policy.
- [24:07]
A
Then what?
- [24:08]
B
Then it extracts a few key fields, depending on the class. If it's a contract, part parties, effective date, renewal terms. If it's an invoice, vendor amount, due date. If it's a report, top line metrics and risks.
- [24:24]
A
And then store all of that in memory.
- [24:27]
B
Exactly. With ALMA embeddings. If you're local first.
- [24:32]
A
So a week later you can ask. Show me every contract with AutoRenew in
- [24:36]
B
the next 60 days and get an answer immediately.
- [24:40]
A
That's brilliant.
- [24:43]
B
Research assembly line.
- [24:45]
A
Oh, I love this one already.
- [24:48]
B
Agent A collects PDFs and tags them by topic.
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A
Agent B summarizes each one and extracts evidence statements.
- [24:56]
B
Agent C compares claims across sources and builds a contradiction matrix.
- [25:01]
A
Slightly nerdy. I approve.
- [25:04]
B
Then agent D writes the final brief with citations.
- [25:08]
A
That's a full research workflow in four agents and sessions.
- [25:12]
B
Attachments make this clean because each stage can pass a file payload to the next stage without weird external plumbing. SecureOps companion.
- [25:24]
A
Sounds serious.
- [25:25]
B
It is. Every deployment starts with OpenClaw. Config validate JSON in CI gatekeeper step. Right. Then any action requiring credentials uses secret ref. If unresolved, fail fast. No fallback, no silent defaults.
- [25:45]
A
Good.
- [25:46]
B
Add streaming partial in telegram so operators see progress live during long tasks.
- [25:51]
A
So people don't think the bot froze.
- [25:54]
B
Exactly. And if you need escalation, send multimedia Status snapshots to Slack or Discord using the shared payload path.
- [26:03]
A
That's operational clarity, not just convenience.
- [26:07]
B
That's the whole point of this release. These features combine.
- [26:12]
A
They aren't isolated checkboxes.
- [26:15]
B
Exactly. If you only adopt one feature, you'll get value. But if you compose three or four, you get a system.
- [26:24]
A
And systems are where the compounding happens.
- [26:27]
B
Every week. You save a little time, avoid a little risk, capture a little more memory.
- [26:33]
A
Then six months later, you've built something quietly formidable.
- [26:36]
B
Quietly formidable is my favorite category of software.
- [26:41]
A
Same. Let's talk about how people are actually using this stuff.
- [26:45]
B
Okay.
- [26:47]
A
The PDF tool alone opens up so many use cases.
- [26:51]
B
I keep thinking about the contract review case.
- [26:54]
A
Right. You upload a vendor contract, you. You ask, are there any unusual termination clauses?
- [27:01]
B
The assistant reads it, analyses it, flags anything weird.
- [27:06]
A
That's a real workflow for freelancers, small
- [27:08]
B
businesses, or the invoice matching case.
- [27:12]
A
Upload an invoice, upload a po. Does this match? What's the difference?
- [27:18]
B
That's accounting automation. No more manually comparing numbers and memory. Alma. Memory embeddings. People are building second brains.
- [27:30]
A
Exactly. You feed it documents, you ask it questions later.
- [27:35]
B
What did we decide about the marketing budget last month?
- [27:39]
A
It searches your local memory.
- [27:41]
B
It answers, that's not science fiction anymore. That's this release and sessions attachments, multi agent pipelines. One agent fetches a document, another summarizes it, another extracts action items.
- [27:58]
A
That's a workflow engine built on OpenCLA. People are building some creative stuff.
- [28:04]
B
I saw someone mention a local research assistant. PDF papers, summarize, store in memory, ask questions later.
- [28:13]
A
That's exactly the use case this release enables.
- [28:17]
B
And it's all local. No data leaves the machine. That's the privacy angle for people who care about that. And there's a growing number. This is the release because you get
- [28:29]
A
GPT4 class capabilities with local privacy.
- [28:33]
B
That's a powerful combination.
- [28:36]
A
It really is. Here's another personal knowledge management.
- [28:41]
B
Tell me more.
- [28:43]
A
You have a folder of PDFs, books, articles, notes, whatever. You feed them into the system, the
- [28:50]
B
PDF tool reads them, the memory system stores what matters.
- [28:55]
A
Then you ask, what did I read about the French Revolution?
- [28:59]
B
It answers, from your personal library.
- [29:01]
A
That's a personal Wikipedia. That knows exactly what you've read.
- [29:06]
B
That's actually really cool.
- [29:08]
A
Quick bonus use case before we wrap this section. Internal policy Q and A.
- [29:13]
B
That's a great one.
- [29:15]
A
Teams upload handbook PDFs, security policies, onboarding docs.
- [29:21]
B
The assistant answers questions with citations. And when policy updates land the memory Index refreshes.
- [29:28]
A
Suddenly people stop deeming ops for every
- [29:31]
B
tiny policy question and ops gets their afternoon back. And it's all local, private, personal, powerful.
- [29:40]
A
That's the promise.
- [29:41]
B
And this release delivers it.
- [29:44]
A
Let's close with a practical checklist.
- [29:47]
B
Sure.
- [29:49]
A
1. If you work with documents, try the PDF tool. Point it at something real, ask it questions.
- [29:56]
B
2. If you care about privacy, set up ALMA memory embeddings. Get your full local stack running. 3. If you use subagents, try passing a file. See what a multi agent pipeline feels like.
- [30:11]
A
4. If you deploy OpenClaw, run OpenClaw config. Validate JSON before you start. Catch the errors early. 5. If you use Telegram, enjoy the streaming default. It's much better.
- [30:25]
B
6. If you're on Zalo, try the rebuilt plugin. Let them know how it performs.
- [30:31]
A
7. If you're building plugins, check out the STT API. See what you can add.
- [30:37]
B
8. Review your secret ref usage. Make sure you're using the failfast behavior.
- [30:44]
A
That's a lot of new stuff in one release.
- [30:47]
B
It is, but it all fits together. How so?
- [30:52]
A
Documents feed into memory, memory powers agents. Agents use secrets. Secrets protect everything.
- [31:01]
B
It's an architecture.
- [31:03]
A
It is. And that's what I keep coming back to. This release isn't about one big feature. It's about completing the architecture.
- [31:12]
B
The document and memory platform.
- [31:15]
A
Exactly.
- [31:16]
B
OpenClaw is becoming the system you build
- [31:18]
A
on, not just the assistant you chat with.
- [31:22]
B
Right. It's the infrastructure underneath.
- [31:26]
A
And that's a wrap. Thanks for listening, everyone. See you next time.
- [31:31]
B
If you try this release, pick one new capability and go deep PDF tool, local memory subagent pipelines. Pick the one that matches what you're building.
- [31:43]
A
Small experiments compound. You'll find the workflow that clicks.
- [31:47]
B
And when it does, let the community know. That's how we all learn.
- [31:53]
A
We'll be back with more. Until then, build something that matters. Bye, everybody.
- [31:59]
B
Bye, folks. Keep shipping.