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How Writer has built an enterprise platform Blueprint that does the AI for you

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Agentic AI continues to grow as enterprises explore its potential. However, there can be pitfalls when building an AI agent workflow. 

May Habib, co-founder and CEO of full-stack AI platform Writer, said there are four things enterprises should consider when thinking about autonomous AI and the automated workflows that AI agents enable. 

“If you don’t focus on the capabilities that are right for you to create self-sufficiency, you’ll never get to a generative AI program that is scaling,” Habib said. 

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For Habib, enterprises need to think about these four things when approaching AI workflows that offer value to them:

  1. Understanding your use cases and the mission-critical business logic connected to those use cases
  2. Knowing your data and the ability to keep the data associated with business cases fresh
  3. Learn who the people that can build those use cases in the team
  4. Managing the capacity of your organization to absorb change

Know your process and build a pipeline

When it comes to understanding use cases, Habib said many enterprises don’t need an AI that will tell them how to grow their business. They need AI that streamlines the work they already do and supports the processes they already have. Granted, of course, the organizations are aware of what these processes are. 

“Never forget that the nodes of the workflow are the hardest part, and not to get overly excited about the hype of agentic until you’ve nailed that workflow, because you are just moving inaccurate information or bad outputs from the system,” Habib said. 

Business processes cannot work without good data, but Habib said businesses should also build a data pipeline to bring fresh data related to the specific business use case. 

Habib said it’s equally important to know who can build the AI applications in an organization and the people who understand the workflows involved in the use cases best. She said AI does not dictate processes; the enterprises dictate the processes AI should follow. All of these culminate in the fourth tenet of effective generative AI: knowing how much change the organization can take and understanding how the actual users of the applications can find value in the technology. 

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Envisioning automated AI workflows

Writer has built AI agents and other applications on its full-stack AI platform. That includes its Palmyra family of models that are specifically designed for enterprises. Its latest model release, Palmyra X 004, excels in function calling and workflow execution, which helps build AI agents. Its AI models also proved very successful for healthcare and finance use cases. Writer also offers RAG frameworks for enterprises. 

Habib said Writer wants to bring more of its vision of agentic AI — though she personally does not like the word agents because it means too many different things — that involves “AI that is able to respond to a command and then go use Writer apps, know how to interact with each other and use third-party applications.” 

Writer’s agentic AI workflow framework relies on a series of Writer apps embedded in enterprise workflows. For example, suppose a customer wants to bring a product to market. In that case, a user can tell their catalog platform running on Writer’s models and applications to pull up the specific product they want, say it needs to be posted on e-commerce sites like Amazon and Macy’s, and include other product information. The agentic workflow will then pull up the product, connect to Amazon and Macy’s APIs and post the product for sale. 

“If it has a GUI, if it has a UI, AI will become a power agent. To us, agentic AI is the ability for AI to use AI plus third-party software and be able to reason its way through,” she said. 

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Moving agentic AI forward

To help facilitate the expansion of its agentic AI vision, Writer announced it raised $200 million in series C funding, bringing its valuation to $1.9 billion. 

Premiji Invest, Radical Ventures and IOCNIQ Growth led the funding round. Other investors included Salesforce Ventures, Adobe Ventures, B Capital, Citi Ventures, IBM Ventures and Workday Ventures, along with existing investors in the company. 

Habib said the new round allows it to continue building on Writer’s existing work with design partners and other customers to bring the automated workflows to life. 


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Lyten buys battery manufacturing assets from beleaguered Northvolt

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Cuberg's old manufacturing facility stands against a blue sky.

Lyten, a Silicon Valley battery startup, announced today that it’s acquiring manufacturing assets from Northvolt, a Swedish battery manufacturer that’s facing a cash crunch.

As part of the deal, Northvolt is selling manufacturing equipment the company inherited in its 2021 acquisition of Cuberg, another battery startup. Lyten will also assume the lease of Cuberg’s old manufacturing facility in San Leandro, California. Lyten will invest $20 million next year to expand facilities in San Leandro and its existing operations in San Jose.

Neither Lyten nor Northvolt immediately replied to questions about the deal’s financial terms.

Unlike many other battery manufacturers, Lyten isn’t relying on nickel, cobalt, manganese, or even iron for its cathode materials. Instead, it’s using cheap and abundant sulfur mixed into a graphene matrix. On the anode side, it doesn’t use any graphite, a material that faces export restrictions from China. The company says the combination results in cells that have greater energy density than nickel-manganese-cobalt flavors but are cheaper to produce than low-cost lithium-iron-phosphate.

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Northvolt has been struggling lately. The company has struggled to scale up production of lithium-ion batteries, and it missed delivery of a large order from BMW, which nudged the automaker to nullify a €2 billion contract. 

To conserve cash, the company announced in August that it would shutter research and development at the Cuberg site, laying off nearly 200 employees. Then in September, it said that it was laying off an additional 1,600 employees, about 20% of its workforce, and that it had halted two planned factory expansions.

It’s unclear whether that cost-cutting and deal with Lyten will be enough to help Northvolt get through the coming year. Last week, Bloomberg reported that Northvolt needs to raise nearly $1 billion to give it some breathing room; the company’s operations reportedly burn through about $100 million a month.

While Northvolt is on the skids, Lyten appears ascendent.

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The San Jose-based startup is planning to break ground next year on a factory in Nevada with a planned capacity of 10 gigawatt-hours. When complete, the $1 billion facility will produce lithium-sulfur batteries destined for micromobility vehicles like scooters and e-bikes, and defense and space applications like drones and satellites. The company expects it to come online in 2027.

Lyten’s purchase of Northvolt’s Cuberg assets give it the equipment and space to produce up to 200 megawatt-hours of lithium-sulfur batteries in the Bay Area. That should give the company some revenue while it prepares its larger factory in Nevada.

Lyten has raised $476 million to date at a $1.17 billion valuation, according to PitchBook, including a $200 million round that closed last year.

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OpenAI reportedly plans to launch an AI agent early next year

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OpenAI reportedly plans to launch an AI agent early next year

OpenAI is preparing to release an autonomous AI agent that can control computers and perform tasks independently, code-named “Operator.” The company plans to debut it as a research preview and developer tool in January, according to Bloomberg.

This move intensifies the competition among tech giants developing AI agents: Anthropic recently introduced its “computer use” capability, while Google is reportedly preparing its own version for a December release. The timing of Operator’s eventual consumer release remains under wraps, but its development signals a pivotal shift toward AI systems that can actively engage with computer interfaces rather than just process text and images.

All the leading AI companies have promised autonomous AI agents, and OpenAI has hyped up the possibility recently. In a Reddit “Ask Me Anything” forum a few weeks ago, OpenAI CEO Sam Altman said “we will have better and better models,” but “I think the thing that will feel like the next giant breakthrough will be agents.” At an OpenAI press event ahead of the company’s annual Dev Day last month, chief product officer Kevin Weil said: “I think 2025 is going to be the year that agentic systems finally hit the mainstream.”

AI labs face mounting pressure to monetize their costly models, especially as incremental improvements may not justify higher prices for users. The hope is that autonomous agents are the next breakthrough product — a ChatGPT-scale innovation that validates the massive investment in AI development.

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Robotic AI performs successful surgery after watching videos for training

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robot surgery

Watching old episodes of ER won’t make you a doctor, but watching videos may be all the training a robotic surgeon’s AI brain needs to sew you up after a procedure. Researchers at Johns Hopkins University and Stanford University have published a new paper showing off a surgical robot as capable as a human in carrying out some procedures after simply watching humans do so.

The research team tested their idea with the popular da Vinci Surgical System, which is often used for non-invasive surgery. Programming robots usually requires manually inputting every movement that you want them to make. The researchers bypassed this using imitation learning, a technique that implanted human-level surgical skills in the robots by letting them observe how humans do it.

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Strava adds Night and Weekly Heatmaps to its fitness app

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Strava adds Night and Weekly Heatmaps to its fitness app

Strava, a popular app for tracking fitness activities, is expanding its Hatmaps feature to help improve the safety of its users. The update should be especially useful now for users in the Northern Hemisphere, which is heading into winter with reduced daylight.

The new Night and Weekly Heatmaps were announced by the San Francisco-based company on Wednesday and are available to all Strava subscribers. As the name of the feature suggests, the Heatmaps show where Strava users are choosing to exercise, with dark thick lines showing well-used routes, and light thin lines showing less popular ones.

First up, the new Night Heatmaps feature is ideal for those who are doing their activities in the late evening or early morning hours, when there’s less light. They show the most popular areas for outdoor activities from sunset to sunrise, helping athletes to better plan their outdoor activities during this time frame. If it’s a new area for you, you may also want to cross-check the Night Heatmap data with Google Street View images to get a better understanding of the place.

Weekly Heatmaps, on the other hand, show data for recent heat from the last seven days so that users can see which trails and roads are currently active, particularly during seasonal transitions when conditions may be impacted by weather.

“Our global community powers ourHeatmaps and now we’ve made it easier for our community members to build routes with confidence, regardless of the season or time of day,” Matt Salazar, Strava’s chief product officer, said in Wednesday’s announcement about the new features. “We are continually improving our mapping technology to make human-powered movement easier for all skill levels.”

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Strava has also shared a useful at-a-glance guide to all four of its Heatmaps, Night, Weekly, Global, and Personal:

Night (new): Discover the most frequented areas between sunset and sunrise; ideal for evening or early morning users.

Weekly (new): Stay updated with the latest data from the past seven days; perfect for adjusting plans around seasonal changes or unexpected closures.

Global (existing): Viewable by anyone regardless of whether you have a Strava account, the Global Heatmap allows you to see what areas are most popular around the world based on community activity uploads.

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Personal (existing): A one-of-a-kind illustration showing the record of everywhere you’ve logged a GPS activity. This heatmap is private and only available to you.






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Google’s new AI model is here to help you learn

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Google's new AI model is here to help you learn

Google’s Gemini is useful as an educational tool to help you study for that exam. However, Gemini is sort of the “Everything chatbot” that’s useful for just about everything. Well, Google has a new model for people looking for more of a robust educational tool. Google calls it Learn About, and it could give other tools a run for their money.

Say what you want about Google’s AI, the company has been hard at work making AI tools centered around teaching rather than cheating. For example, it has tools in Android studio that guides programmers and help them learn coding. Also, we can’t forget about NotebookLM. This is the tool that takes your uploaded educational content and helps you digest it. We can’t forget abou the Audio Overviews feature that turns your uploaded media into a live podcast-style educational discussion.

So, Google has a strong focus on education with its AI tools. Let’s just hope that other companies will follow suit.

Google’s new AI tool is called Learn About

This tool is pretty self-explanatory, as it focus on giving you more text-book style explanations for your questions. Rather than simply giving you answers, this tool will go the extra mile to be more descriptive with its explanation. Along with that, Learn About will also provide extra context on the subject and give you other educational material on it.

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Google achieved this by using a totally different model to power this tool. Rather than using the Gemini model, Ask About uses a model called LearnLM. At this point, we don’t really know much about this model, but we know that Google steered it more towards providing academic answers.

Gemini’s answer vs. Learn About’s answers

We tested it out by asking what pulsars are, and we compared the answer to what Gemini gave us for the same question. Gemini delivered a pretty fleshed-out explanation in the form of a few paragraphs. It also snagged a few pictures from the internet and pasted the link to a page at the bottom. This is good for a person who’s casually looking up a definition. Maybe that person isn’t looking to learn the ins and outs of what a pulsar is.

There was one issue with Gemini’s answer; one of the images that it pasted was an image of a motorcycle. It pasted an image of the Bajaj Pulsar 150. So, while it technically IS a pulsar, a motorcycle shares very few similarities with massive rapidly spinning balls of superheated plasma billions of miles away from Earth.

What about Learn About?

Learn About also gave an explanation in the form of a few paragraphs;  however, Learn About’s explanation was shorter. It makes up for it by producing more extraneous material. Along with images, it provided three links (one of which was a YouTube video) and chips with commands like Simplify, Go deeper, and Get images (more on the chips below).

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Under the chips, you’ll see suggestions of other queries that you can put in for additional context. Lastly, in textbook style, you’ll see colored blocks with additional content. For example, there’s a Why it matters block and a Stop & think block.

Chips

Going back to the aforementioned chips, selecting Simplify and Get images are axiomatic enough. Tapping/clicking on the Go Deeper chip is a bit more interesting. It brought up an Interactive List consisting of a selection of additional queries that will provide extra information about pulsars. Each query you select will bring up even more information.

Google Ask about 6

Textbook blocks

Think about the textbooks you used in school, and you’ll be familiar with these blocks. These blocks come in different colors. The Why it matters block tells you why this information is important. Next, the Stop & think block seems to give you a little bit of tangential information. It asks a question and has a button to reveal the answer. It’s a way to get you to think outside of the box a bit.

There’s a Build your vocab box that introduces you to a relevant term and shows you a dictionary-style definition of it. This is a term that the reader is most likely not familiar with.

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The next block we encountered was the Test your knowledge block. This one has a quiz-style question and it gives you two options. Other subject matters might have more choices, but this is what we got in our usage.

We also saw a Common misconception block. This one pretty much explains itself.

Bottom bar

At the very bottom of the screen, you’ll see a bar with some additional chips. One chip should show the title of the current subject, and Tapping/clicking on it will bring up a floating window with additional topic suggestions. In our case, we also saw the interactive list that we saw previously. This one will show the list in a floating window.

One issue

So, do you remember when Gemini gave us the image of the motorcycles? Well, while the majority of Learn About’s images were relevant to the subject, it still retrieved two images of the motorcycles. As comical as it is, it shows that Google’s AI still has a ways to go before it’s perfect. However, barring that little mishap, Learn About runs as smoothly as the motorcycle it’s surfacing pictures of.

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Use it today!

You can use Learn About today if you want to try it out. Just go to the Learn About website Learn About website, and you’ll be able to try it out. Just know that, as with most Google services, the availability will depend on your region. We were able to access it in the U.S. in English. Just know that you may not have it in regions that Google typically overlooks.

You can use it regardless of if you’re a free or paid user. Please note that Learn About is technically an experiment. This means that Google only put this on the market for testing. Google could potentially lock this behind a paywall after the beta testing phase. Just know that this feature could disappear down the line. So, you’ll want to get in and use it while you can.

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GOG’s preservation label highlights classic games it’s maintaining for modern hardware

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GOG's preservation label highlights classic games it's maintaining for modern hardware

GOG is launching an effort to help make older video games playable on modern hardware. The will label the classic titles that the platform has taken steps to adapt in order to make them compatible with contemporary computer systems, controllers and screen resolutions, all while adhering to its DRM-free policy. The move could bring new life to games of decades past, just as GOG did two years ago with a refresh of . So far, 92 games have received the preservation treatment.

“Our guarantee is that they work and they will keep working,” the company says in the video announcing the initiative.

Preservation has been a hot topic as more games go digital only. Not only are some platforms disk drives by default, but ownership over your library is more ephemeral than it seems. After all, most game purchases are , and licenses can be revoked (as The Crew players know ).

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