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Agents need vector search more than RAG ever did

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What’s the role of vector databases in the agentic AI world? That’s a question that organizations have been coming to terms with in recent months.

The narrative had real momentum. As large language models scaled to million-token context windows, a credible argument circulated among enterprise architects: purpose-built vector search was a stopgap, not infrastructure. Agentic memory would absorb the retrieval problem. Vector databases were a RAG-era artifact.

The production evidence is running the other way.

Qdrant, the Berlin-based open source vector search company, announced a $50 million Series B on Thursday, two years after a $28 million Series A. The timing is not incidental. The company is also shipping version 1.17 of its platform. Together, they reflect a specific argument: The retrieval problem did not shrink when agents arrived. It scaled up and got harder.

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“Humans make a few queries every few minutes,” Andre Zayarni, Qdrant’s CEO and co-founder, told VentureBeat. “Agents make hundreds or even thousands of queries per second, just gathering information to be able to make decisions.”

That shift changes the infrastructure requirements in ways that RAG-era deployments were never designed to handle.

Why agents need a retrieval layer that memory can’t replace

Agents operate on information they were never trained on: proprietary enterprise data, current information, millions of documents that change continuously. Context windows manage session state. They don’t provide high-recall search across that data, maintain retrieval quality as it changes, or sustain the query volumes autonomous decision-making generates.

“The majority of AI memory frameworks out there are using some kind of vector storage,” Zayarni said. 

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The implication is direct: even the tools positioned as memory alternatives rely on retrieval infrastructure underneath.

Three failure modes surface when that retrieval layer isn’t purpose-built for the load. At document scale, a missed result is not a latency problem — it is a quality-of-decision problem that compounds across every retrieval pass in a single agent turn. Under write load, relevance degrades because newly ingested data sits in unoptimized segments before indexing catches up, making searches over the freshest data slower and less accurate precisely when current information matters most. Across distributed infrastructure, a single slow replica pushes latency across every parallel tool call in an agent turn — a delay a human user absorbs as inconvenience but an autonomous agent cannot.

Qdrant’s 1.17 release addresses each directly. A relevance feedback query improves recall by adjusting similarity scoring on the next retrieval pass using lightweight model-generated signals, without retraining the embedding model. A delayed fan-out feature queries a second replica when the first exceeds a configurable latency threshold. A new cluster-wide telemetry API replaces node-by-node troubleshooting with a single view across the entire cluster.

Why Qdrant doesn’t want to be called a vector database anymore

Nearly every major database now supports vectors as a data type — from hyperscalers to traditional relational systems. That shift has changed the competitive question. The data type is now table stakes. What remains specialized is retrieval quality at production scale.

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That distinction is why Zayarni no longer wants Qdrant called a vector database.

“We’re building an information retrieval layer for the AI age,” he said. “Databases are for storing user data. If the quality of search results matters, you need a search engine.”

His advice for teams starting out: use whatever vector support is already in your stack. The teams that migrate to purpose-built retrieval do so when scale forces the issue.

“We see companies come to us every day saying they started with Postgres and thought it was good enough — and it’s not.”

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Qdrant’s architecture, written in Rust, gives it memory efficiency and low-level performance control that higher-level languages don’t match at the same cost. The open source foundation compounds that advantage — community feedback and developer adoption are what allow a company at Qdrant’s scale to compete with vendors that have far larger engineering resources.

“Without it, we wouldn’t be where we are right now at all,” Zayarni said.

How two production teams found the limits of general-purpose databases

The companies building production AI systems on Qdrant are making the same argument from different directions: agents need a retrieval layer, and conversational or contextual memory is not a substitute for it.

GlassDollar helps enterprises including Siemens and Mahle evaluate startups. Search is the core product: a user describes a need in natural language and gets back a ranked shortlist from a corpus of millions of companies. The architecture runs query expansion on every request – a single prompt fans out into multiple parallel queries, each retrieving candidates from a different angle, before results are combined and re-ranked. That is an agentic retrieval pattern, not a RAG pattern, and it requires purpose-built search infrastructure to sustain it at volume.

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The company migrated from Elasticsearch as it scaled toward 10 million indexed documents. After moving to Qdrant it cut infrastructure costs by roughly 40%, dropped a keyword-based compensation layer it had maintained to offset Elasticsearch’s relevance gaps, and saw a 3x increase in user engagement.

“We measure success by recall,” Kamen Kanev, GlassDollar’s head of product, told VentureBeat. “If the best companies aren’t in the results, nothing else matters. The user loses trust.” 

Agentic memory and extended context windows aren’t enough to absorb the workload that GlassDollar needs, either.

 “That’s an infrastructure problem, not a conversation state management task,” Kanev said. “It’s not something you solve by extending a context window.”

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Another Qdrant user is &AI, which is building infrastructure for patent litigation. Its AI agent, Andy, runs semantic search across hundreds of millions of documents spanning decades and multiple jurisdictions. Patent attorneys will not act on AI-generated legal text, which means every result the agent surfaces has to be grounded in a real document.

“Our whole architecture is designed to minimize hallucination risk by making retrieval the core primitive, not generation,” Herbie Turner, &AI’s founder and CTO, told VentureBeat. 

For &AI, the agent layer and the retrieval layer are distinct by design.

 “Andy, our patent agent, is built on top of Qdrant,” Turner said. “The agent is the interface. The vector database is the ground truth.”

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Three signals it’s time to move off your current setup

The practical starting point: use whatever vector capability is already in your stack. The evaluation question isn’t whether to add vector search — it’s when your current setup stops being adequate. Three signals mark that point: retrieval quality is directly tied to business outcomes; query patterns involve expansion, multi-stage re-ranking, or parallel tool calls; or data volume crosses into the tens of millions of documents.

At that point the evaluation shifts to operational questions: how much visibility does your current setup give you into what’s happening across a distributed cluster, and how much performance headroom does it have when agent query volumes increase.

“There’s a lot of noise right now about what replaces the retrieval layer,” Kanev said. “But for anyone building a product where retrieval quality is the product, where missing a result has real business consequences, you need dedicated search infrastructure.”

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How CIOs can create a strong foundation for an AI-enabled workplace

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As with any new tech, there’s a scale for AI adoption among businesses leaving some are ahead of the curve and others much further behind as they continue to resist and delay.

But what’s clear is that adoption is happening with or without formal strategy because nearly two-thirds (65%) of employees now say they intentionally use AI for work.

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OpenAI purchases online tech talk show TBPN

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OpenAI said the purchase will be part of its strategy to further the conversation on the changes brought about by artificial intelligence.

OpenAI, in what is being described as an unusual move, is set to purchase the Technology Business Programming Network (TBPN), a daily, live tech talk show hosted by Jordi Hays and John Coogan, that often features high-profile tech leaders and entrepreneurs. OpenAI 

OpenAI’s chief executive officer of applications Fidji Simo said: “As I’ve been thinking about the future of how we communicate at OpenAI, one thing that’s become clear is that the standard communications playbook just doesn’t apply to us. We’re not a typical company.

“We’re driving a really big technological shift. And with our mission to ensure artificial general intelligence benefits all of humanity comes a responsibility to help create a space for a real, constructive conversation about the changes AI creates, with builders and people using the technology at the centre.”

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While the full details of the deal have yet to be disclosed, OpenAI said the TBPN team will maintain editorial independence and make decisions on their guests and programming. According to the Wall Street Journal, TBPN stated that it generated $5m in advertising revenue last year and is on track to exceed $30m in revenue in 2026.

However, an OpenAI spokesperson told Bloomberg that the platform is not aiming to make TBPN a money-making enterprise. 

In a statement, Hays expressed excitement at the venture, while making note of the importance of a strong partnership where both parties work as a team to communicate change and innovation in the AI and tech spaces. 

He said: “While we’ve been critical of the industry at times, after getting to know Sam and the OpenAI team, what stood out most was their openness to feedback and commitment to getting this right. Moving from commentary to real impact in how this technology is distributed and understood globally is incredibly important to us.”

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Earlier this week OpenAI closed a larger than expected funding round in which it raised $122bn, exceeding the projected figure of $110bn. Part of that funding is expected to be put towards the scale and growth of the platform’s AI technologies and research, in line with current global demands. 

Don’t miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic’s digest of need-to-know sci-tech news.

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This State Has Costco’s First Stand Alone Gas Station

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The best thing about retail warehouse stores is obviously the selection. After all, where else can you buy a new T-shirt, birthday cake, and a set of tires on the same day? But the ability to fill up with gas before leaving the parking lot is a plus as well. That’s why stores like Costco, where you can use these tips to save time at the pump, are so convenient. But now the company is moving forward with standalone gas stations, and the company’s first in California is members-only.

Members will need to insert or scan their membership card to refuel, just as they would at Costco’s attached gas stations. However, non-members may be able to access the pumps using a Costco Shop card, as they currently can at on-site locations. Costco’s new gas station is located in Mission Viejo, California, and it’s a 17,000 square foot facility operated by company employees. It has 40 pumps covered by a large canopy, and it will run from 5 a.m. to 10 p.m. daily, Sunday through Saturday.

The station is expected to open by the end of June 2026. But if you don’t live in California, you may not have to wait long. Costco is planning to build more standalone gas stations, beginning in Honolulu, Hawaii. As of this writing, the company hasn’t publicly addressed this new program. But the belief is that stand alone stations can help reduce the heavy traffic flow that currently plagues many on-site locations.

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Costco’s gas boom and competitive pricing strategy

Costco’s first standalone gas station (which will also strategically stay cheaper than most) was initially announced in the summer of 2025. The facility is located off Interstate 5 in Mission Viejo, California, at the site where a Bed Bath & Beyond once stood. At the time of the announcement, the company’s gas stations were experiencing a boom in business, thanks mostly to extended operating hours. The decision to move forward with a new test store may have been influenced by this positive reaction.

Costco members get access to gas prices that can often beat other competitors by anywhere from 10 to 25 cents per gallon. This is possible because of the company’s warehouse approach, which includes buying fuel in large quantities. Costco also works directly with suppliers to get the best cost and then passes that savings on to its members. 

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Costco’s first gas station opened in 1995 and since then, their fuel business has grown. The company currently has over 700 stations around the world, serving millions of paid members every day. Those members can use the Costco app to check fuel prices in real time, as well as store hours, and locations near them.



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How To Know When It’s Time To Turn On Your Lawn Sprinklers This Spring

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Getting a lush, green lawn sometimes requires a bit of help. This is where a lawn sprinkler system, be it an energy-saving smart sprinkler system or a more traditional setup, comes into the picture by providing a yard with sufficient moisture for sustained growth. Installing such a system is just the start, though, and it’s also crucial to know how to use it to the fullest. That means knowing the right time of year to power it up, which isn’t necessarily a specific day or month. Instead, it’s a decision that’s largely predicated on environmental factors that make it clear winter has come and gone, and that spring is finally in bloom.

First and foremost is the temperature. It’s recommended that a sprinkler system only be activated in spring once daily temperatures are higher than 40 degrees Fahrenheit for 10 days or longer. This way, you know for certain spring is here and you’re not experiencing a random warmer day within an overall cold period. In a similar vein, the ground itself should be completely thawed and free of frost, further indicating that sprinkler season has arrived. No matter where you live, you should also refer to previous years’ weather patterns to get a rough idea of when the final snowfalls and freezes usually happen. Some news outlets may also offer estimated dates for these, so be sure to check around.

If all else fails and you’re unsure whether it’s a good time to turn on your sprinklers, there’s no shame in playing it safe and waiting until temperatures are consistently warm and the last vestiges of winter are long gone. After all, erring on the side of caution is preferred to activating your system too early and suffering the consequences.

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Why lawn sprinkler timing is so important

Turning on your lawn sprinkler system is anything but an arbitrary decision. It needs to happen when the environment is just right, or else there could be serious consequences. For one, it’s no secret that running and leaving water through unprepared pipes in freezing conditions can lead to damage. This water freezes, expands, and cracks pipes and fittings. If you manage to avoid pipe or sprinkler damage, you’re still at risk of shortening the lifespan of the system by running it when it’s not necessary. The longer you run your system, the more wear and tear it endures, potentially leading to it failing sooner than it should.

The consequences of activating a sprinkler system early go beyond the health of the system itself. Ice and snow melt takes time to soak into the ground, so any excess water from a sprinkler system may lead to sogginess and puddles at best, or leave your grass susceptible to disease at worst. Not to mention, running your sprinklers more than necessary will, of course, lead to a higher water bill. Thus, don’t be afraid to show some restraint, even if it looks like your lawn is in need of watering right out of winter.

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Lawn care can very easily go wrong. There are many mistakes everyone makes with lawn mowers, for instance, and homeowners can also turn on their lawn sprinklers at the wrong time of year. That’s why it’s key to keep an eye on the weather and sustained temperatures before officially beginning your spring watering.



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Maker 3D-Prints Shoes Layer by Layer, Successfully Goes from Printer to Pavement

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3D-Printed Shoes
People are constantly pushing the boundaries of 3D printing, but shoes have long been the holy grail, or rather the holy nightmare, of the technology. They must be able to bend with each step, provide traction on a variety of surfaces, and withstand regular use without falling apart at the seams. DaveRig Design took on this exact task in a recent project, resulting in a pair of casual shoes that look and feel right at home on the street.



He started with the CityStep casual everyday sneaker design, which you can get at MakerWorld. This design features a slip-on form with a contoured profile that wraps around your foot snugly at the back and sides, while leaving the top of the shoe open and breathable. The design features a dense infill pattern on top to give it a knit fabric look and feel; there are no separate parts or glue jobs necessary, and the greatest part is that each shoe prints upright in one piece with a tiny heel stand to protect it from tumbling over while printing. Print times on typical machines are roughly sixty-six hours each pair, so you’re looking at around seventy-six hours on some machines due to the fine details and support structures.


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3D-Printed Shoes
The actual game changer was the material he chose. DaveRig chose BIQU MorPhlex filament, a flexible choice that handles like ordinary TPU out of the spool, with a hardness of roughly 90 A, which is rigid enough to keep the printer from stringing and jamming, which is a common problem with softer filaments. Once the print is completed and the material has cooled, it transitions to a considerably softer seventy-five A rubber-like feel that provides cushioning and traction without the need for any additional post-processing gimmicks. He was using a Snapmaker U1 tool changer, a machine designed to automatically swap between four separate extruders, which came in handy for a project that required over three thousand swaps to blend colors and hardness levels across different parts of the shoe, ensuring that the sole remained grippy, the midsection flexed naturally, and the upper remained light and airy all at once.

3D-Printed Shoes
Before sending it to the printer, he spent some time in Blender fine-tuning the model, making subtle changes to get the layer bonding just perfect so the finished shoes wouldn’t split when stretched over your foot. Supports were made with a combination of flexible filament and conventional PLA to make them easy to remove when the print was completed, and he strengthened them to keep them from shifting around during the long print. To ensure perfect colour consistency, he ran both shoes side by side on the same build plate.

3D-Printed Shoes
When the print was finally completed and the supports were removed without a hitch, the results were a pleasant surprise, nearly factory-fresh polished. The upper has a nice textured surface that smoothes over the layer lines so they are scarcely noticeable, and they appear to have come off a production line rather than a homemade work. The sole provides just enough traction, the MorPhlex’s post-print softness makes it easy to grab surfaces, and the heel cup keeps everything held in place without slipping around during normal walking.

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CFTC sues three states for trying to regulate prediction markets

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The US Commodity Futures Trading Commission is suing Illinois, Arizona and Connecticut for attempting to outlaw or regulate prediction markets like Kalshi and Polymarket. The CFTC believes it has sole jurisdiction to regulate these platforms, and that states attempting to classify them as illegal gambling are overstepping their authority.

CFTC defines prediction markets as “designated contract markets” where futures contracts are traded, essentially letting people bet on the outcome of events (for example, who will be the Democratic nominee for president in 2028). And because futures contracts are financial instruments distinct from traditional bets, they arguably fall under the supervision of the CFTC rather than the sports gambling authorities of individual states.

Multiple states, including the three the CFTC is suing, have challenged that interpretation of what prediction markets are and how they operate. Nevada sued Kalshi in February for operating a sports gambling market without proper licenses, a lawsuit made possible because a federal appeals court declined to prevent Nevada from pursuing its case. Arizona’s attorney general filed a lawsuit against Kalshi in March along similar illegal sports gambling lines, and because the platform let people bet on Arizona elections, which violates state law. Both Illinois and Connecticut have also sent Kalshi and other prediction markets cease-and-desist letters, ordering them to stop advertising and offering their services in their respective states.

“The CFTC will continue to safeguard its exclusive regulatory authority over these markets and defend market participants against overzealous state regulators,” CFTC Chairman Michael S. Selig said in a statement. “This is not the first time states have tried to impose inconsistent and contrary obligations on market participants, but Congress specifically rejected such a fragmented patchwork of state regulations because it resulted in poorer consumer protection and increased risk of fraud and manipulation.”

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Attempts to regulate, or in this case, stave off regulation of predication markets are complicated by the fact that President Donald Trump’s family has ties to the industry. Donald Trump Jr. is a paid advisor for Kalshi and investor in Polymarket. Major transactions made before recent US military actions in Iran have also suggested that people close to the government might be trading on prediction markets with insider knowledge. Some prediction markets have implemented new rules to prevent insider trading, but given the circumstances, it makes sense that states wouldn’t be satisfied with companies policing themselves.

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Shadow Lord’ season 2 confirmed

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Ahead of its premiere, Dave Filoni has revealed that the Star Wars animated series Maul: Shadow Lord will return for a second season. The Lucasfilm co-president revealed that season 2 is already in the works, telling Esquire that “at the end of the day, people like that character.”

Filoni didn’t reveal any other details about the plot or release date for season 2. However, the news isn’t a great surprise given Lucasfilm’s past history with its animated series — The Clone Wars ran seven seasons, Star Wars Rebels four seasons, Star Wars Resistance two seasons and Star Wars: The Bad Batch three seasons.

Maul: Shadow Lord explores the Zebrak Sith Lord’s story about a year after the time of the Clone Wars. Season 1’s 10 seasons will stream twice a week on Disney+ starting on April 6 and run through May 6. It covers Maul’s plot to rebuild his criminal syndicate “on a planet untouched by the Empire,” according to Lucasfilm. “There, he crosses paths with a disillusioned young Jedi Padawan who may just be the apprentice he is seeking to aid him in his relentless pursuit for revenge.”

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Microsoft no longer wants to borrow its AI, it wants to build it

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Microsoft has been pushing AI on consumers whether they wanted it or not. Given the ferocity with which the company has been pushing AI into its products, you might be surprised to learn that it didn’t use its own AI. It took OpenAI’s technology, wrapped it into Copilot and Teams, and called it a day.

But things are changing. Whether the company noticed the public’s negative reaction to its bloated Windows 11 operating system or saw Linux gaining market share in gaming, Microsoft is finally working to introduce a calmer Windows 11 and focus on developing its own AI models.

As reported by Bloomberg, Mustafa Suleiman, CEO of Microsoft AI, made the ambition clear: “Certainly by 2027, the objective is to really get to state-of-the-art,” covering models that can handle text, images, and audio.

What was stopping Microsoft from doing this sooner?

A contract. Microsoft’s deal with OpenAI previously prevented the company from building its own broadly capable AI models. That clause was removed as part of a renegotiated agreement last year, giving Microsoft the freedom to operate independently.

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The company isn’t starting from zero, either. In October, Microsoft began using a cluster of Nvidia GB200 chips to build the computing power needed for frontier-level AI development. Regarding the timeline, “we’re sort of ramping over the next sort of 12 to 18 months to get to frontier-scale compute,” Suleyman said.

What does this mean for you?

The first sign of this push is here. Microsoft has released a speech transcription model that outperforms rival products in 11 of the 25 most widely spoken languages. It’s built to handle noisy environments and will soon be rolling out to Teams and other Microsoft apps.

The bigger picture is that Microsoft wants long-term AI self-sufficiency. CEO Satya Nadella reinforced the message this week, emphasizing the importance of building state-of-the-art models over the next three to five years.

For everyday users, more competition in AI means better, smarter tools built into the apps you use. On the other hand, it also means another big company exponentially ramping up purchases of GPUs and RAM, which will drive prices for consumer RAM, GPUs, and SSDs even further.

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GeekWire Awards: From the farm to space, Next Tech Titan finalists growing to meet big challenges

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(Company logos)

The path from successful startup to industry heavyweight is often marked by the ability to solve massive, complex problems at scale — whether those challenges are on a farm, battlefield or in low-Earth orbit.

This GeekWire Award, presented by Baird, takes notice of the next dominant force in Pacific Northwest tech. The Next Tech Titan finalists are: Overland AI, Carbon Robotics, Stoke Space, Chainguard and MotherDuck.

Now in its 18th year, the GeekWire Awards is the premier event recognizing the top leaders, companies and breakthroughs in Pacific Northwest tech, bringing together hundreds of people to celebrate innovation and the entrepreneurial spirit. It takes place May 7 at the Showbox SoDo in Seattle.

Last year’s Next Tech Titan winner was Truveta, a Bellevue, Wash.-based company that aims to aggregate medical records data from partner institutions to link treatments with outcomes and underlying health. Truveta raised $320 million in fresh funding in 2025 to push its valuation above $1 billion.

Continue reading for information on the 2026 Next Tech Titan finalists, who were chosen by a panel of independent judges from community nominations. You can help pick the winner: Cast your ballot here or in the embedded form at the bottom. Voting runs through April 10.

Overland AI develops autonomous vehicle software and hardware designed specifically for complex, off-road environments. The company’s platform allows robotic vehicles to navigate high-speed, unpredictable terrain where GPS and cellular signals are often unavailable. Overland is focused on operational integration with the U.S. Army and Marine Corps, and is a key player in the emerging defense-tech corridor of the Pacific Northwest.

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GeekWire first covered Overland AI in 2022 when it was a small, stealthy group of researchers spinning out of the University of Washington’s Robot Learning Laboratory. The company, No. 12 on the GeekWire 200, has grown to more than 100 employees, raised more than $140 million, and opened a 22,000 square-foot production facility in Seattle since then.

Ag-tech startup Carbon Robotics builds AI-powered machinery designed to eliminate weeds without the use of chemical herbicides. Its flagship LaserWeeder uses computer vision to identify and zap weeds with lasers, a process powered by the company’s “Large Plant Model.” This AI model, trained on 150 million labeled plants, allows the machines to adapt to new crops and environments in minutes. The company is also expanding into autonomous farm equipment with its Carbon ATK platform and an unrevealed new AI robot.

Founded in 2018 by Isilon Systems co-founder Paul Mikesell, the Seattle-based company has raised $177 million to date and employs about 260 people. Its LaserWeeders are now active on hundreds of farms across 15 countries, helping growers significantly reduce labor and pesticide costs. Carbon is No. 10 on the GeekWire 200.

Stoke Space is developing Nova, a medium-lift rocket designed for 100% reusability and rapid turnaround between flights. Unlike competitors that focus on heavy-lift vehicles, the Kent, Wash.-based company is targeting the medium-lift market with a unique second-stage design featuring an actively cooled heatshield for atmospheric reentry. The goal is to provide a more flexible and cost-effective launch platform that can be reused as seamlessly as an aircraft.

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Founded by former Blue Origin and SpaceX engineers, Stoke Space has raised $1.34 billion to date, including a massive $860 million Series D round concluded in early 2026. The company, No. 8 on the GeekWire 200, is currently preparing for its first orbital launch from Cape Canaveral later this year and has already been selected by the U.S. Space Force for national security launches.

Chainguard secures the “software supply chain” by protecting the open-source components and container images used in modern cloud applications. The company’s tools allow developers to use verified, vulnerability-free code, automating the process of keeping foundational software secure. By focusing on the root of software production, Chainguard helps engineering teams eliminate security risks without slowing down development cycles.

Founded in 2021 and based in Kirkland, Wash., the startup has raised $892 million to date, reaching a $3.5 billion valuation. In fiscal year 2025, the company grew its annual recurring revenue sevenfold to $40 million. Now employing more than 500 people and serving over 200 customers — including GitLab and Hewlett Packard Enterprise — Chainguard is No. 2 on the GeekWire 200.

MotherDuck provides a serverless analytics platform built on the open-source DuckDB database engine. Designed for “small data” that doesn’t reach petabyte scale, the technology allows users to run fast SQL queries locally in a browser or in the cloud without the complexity of distributed architectures. By merging local processing speed with cloud scalability, the platform aims to make data analysis more cost-effective and accessible.

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Founded in 2022 by former Google BigQuery founding engineer Jordan Tigani, the Seattle startup has raised more than $100 million and is No. 25 on the GeekWire 200.

Astound Business Solutions is the presenting sponsor of the 2026 GeekWire Awards. Thanks also to gold sponsors Amazon Sustainability, BairdBECU, JLLFirst Tech and Wilson Sonsini, and silver sponsors Prime Team Partners.

The event will feature a VIP reception, sit-down dinner and fun entertainment mixed in. Tickets go fast. A limited number of half-table and full-table sponsorships available. Contact events@geekwire.com to reserve a spot for your team today.

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The Galaxy S26 Ultra feels like a software update and that’s why its boring

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There was a time when upgrading to a new flagship phone felt like stepping into something noticeably better. Bigger batteries, sharper cameras, faster charging – real, tangible upgrades that justified both the hype and the price.

The Galaxy S26 Ultra doesn’t quite feel like that moment. It feels like refinement masquerading as reinvention.

On paper, Samsung has done what it always does. The S26 Ultra comes with Qualcomm’s latest Snapdragon 8 Elite Gen 5 chip, delivering roughly a 10% CPU and 15% GPU improvement over last year’s model. It now supports up to 60W wired charging, up from 45W, and introduces features like a privacy display and new AI-powered tools layered across the system.

Individually, these upgrades sound meaningful. Collectively, they don’t feel transformative. Because the fundamentals – the things users actually notice – haven’t really moved.

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The battery is still 5,000mAh. That’s the same capacity Samsung has used across multiple generations, from the S23 Ultra to the S25 Ultra. Charging is faster, yes, but not dramatically so. In real-world terms, you’re saving minutes, not changing behavior. And in some tests, battery performance is only marginally better, largely due to efficiency gains from the new chip rather than any hardware leap.

The camera story is even more telling

The S26 Ultra retains a triple 200-10-50MP setup, with slight tweaks like a wider f/1.4 aperture on the main sensor. But the sensor size remains largely unchanged, and that matters. Competitors like Xiaomi and Vivo have pushed into 1-inch-type sensors, which physically capture more light and detail, especially in low-light conditions. The difference isn’t just technical – it’s visible in depth, dynamic range, and natural detail.

Samsung’s approach, meanwhile, continues to rely heavily on computational photography. The results are still excellent, but they’re also familiar. Bright, sharp, slightly processed images that look good on social media but don’t necessarily push the envelope.

And that’s the recurring theme here: nothing is worse, but nothing is meaningfully better.

So Samsung leans into AI

The S26 Ultra is packed with AI features – image generation, object insertion, real-time editing, writing tools, contextual suggestions. Some of these are genuinely impressive. You can take a photo, remove objects, change lighting conditions, or even insert entirely new elements using generative AI. You can rewrite messages in different tones or generate content directly from prompts.

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Technically, it’s powerful. Practically, it’s complicated.

Because most of these features fall into two categories. The first is automation – things like translation, smart suggestions, or contextual actions. These are useful, but still inconsistent. Voice assistants like Bixby have improved, but they struggle with context and reliability. Ask a complex question, and you might still get an irrelevant answer.

The second category is generative AI – the flashy stuff. Image edits, creative tools, content generation. These are fun, but rarely essential. And there are trade-offs. Many of these tools reduce image resolution, sometimes by as much as 20–30%, or output content that doesn’t match the device’s native display ratio. In some cases, a generated image might come out at 1024×1024 resolution on a phone that has a 2K display.

It’s impressive tech, but it doesn’t always hold up in real use

Which leads to a bigger question. If the most noticeable upgrades are software features that could theoretically roll out to older devices, what exactly are you upgrading for?

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This is where the S26 Ultra starts to feel less like a new phone and more like a software update packaged as hardware. And it’s not just Samsung. This is becoming the direction of the entire industry.

Flagship phones are no longer defined by massive hardware leaps. They’re defined by balance.

The S26 Ultra is arguably the most complete Android phone you can buy. It has a great display, strong battery life, versatile cameras, long-term software support (up to seven years), and one of the most customizable software experiences through One UI. It even includes features no one else offers, like the integrated S Pen.

But in trying to be the perfect all-rounder, it avoids taking risks. It doesn’t have the largest battery. It doesn’t have the biggest camera sensor. It doesn’t have the fastest charging. It doesn’t push any single category to its limit.

Instead, it plays it safe. And safe is starting to feel predictable. Other brands are experimenting more aggressively. Some are pushing camera hardware, others are pushing battery tech or charging speeds. Not all of it works, but it creates a sense of momentum – of progress.

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Samsung, on the other hand, is optimizing rather than reinventing. That makes the S26 Ultra an excellent phone for most people. It does everything well, and for the average user, that’s exactly what matters. The camera is more than good enough. The battery lasts a full day. The performance is smooth. The experience is reliable.

But for anyone looking for something new – something that feels like a leap – it falls short. The irony is that the S26 Ultra proves just how mature smartphones have become. The gaps between generations are shrinking. The need to upgrade every year is disappearing.

And maybe that’s the real takeaway

The Galaxy S26 Ultra isn’t a bad upgrade. It’s just not a necessary one. Because when your biggest innovations feel like features that could have been a software update, it’s a sign that the flagship race isn’t about breakthroughs anymore.

It’s about maintaining perfection. And perfection, as it turns out, can be a little boring.

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