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Kimi K3
00:19:58
Moonshot AI released Kimi K3, a 2.8T-parameter model it says rivals Opus 4.8 and GPT-5.5. Google fell months behind on Gemini 3.5 Pro, MLB banned dugout iPads from accessing GenAI for in-game calls, and The Verge tested Siri AI.
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Welcome to the Tech Brew Ride home for Friday, July 17, 2026. I'm Brian McCullough. Today Moonshot AI released Kimi K3, a new model that they say rivals Opus 4.8 and GPT 5.5. Google has fallen months behind on its Gemini 3.5. Pro Major League Baseball banned dugout iPads from accessing AI in game and of course the weekend long Read suggestions Here's what you missed today in the world of tech.
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Foreign.
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To all the IT pros out there, today's sponsor has a special gift for you. That gift is Ace of Uptime, an online card based game where you go up against the problems that threaten uptime on a daily basis. Each level pits you against a recognizable adversary. We're talking about alert overloads, heat that's trapped in a cramped server room, and systems that look fine but are far from it. Choose your move and see whether your decisions resolve the issue or escalate it. The game is fast, fun, fun and designed to let you test how you'd respond when Uptime is on the line. Think you can beat the villains of downtime? Head over to eaton.com ace to find out. That's eaton.com ace Chinese AI company Moonshot AI has released Kimik 3, a 2.8 trillion parameter AI model that it says rivals Claude Opus 4.8 and GPT 5.5 and plans to release its full model weights by July 27th. Kimmy K3 has taken the number one spot on the front end code arena Benchmark, surpassing Claude Fable 5 and scoring 88.3 on Terminal Bench 2.1, which trails only GPT 5.6 Soul's score of 88.8. Quoting VentureBeat if you want to take Kimmy K3 for a spin right now, you can just head to Kimi.com, sign up with a Google account or phone number, no credit card required, and start chatting with what may be the most powerful open source model ever built. Kimik 3 is a Frontier class large language model with 2.8 trillion total parameters, roughly 75% larger than Deep Seq's V4 Pro, which the company's own timeline chart shows at approximately 1.6 trillion parameters. The model features a 1 million token context window, native visual understanding capabilities, and an always on reasoning mode that the company calls thinking mode. The model is built on two key architectural innovations developed internally at Moonshot AI Kimi Delta Attention, a hybrid linear attention mechanism, and Attention Residuals, which the company describes as a drop in replacement for residual connections that deliver consistent scaling gains. Both techniques were previously published as open research by the MoonShot team on GitHub. On the API side, Kimik 3 is compatible with the OpenAI SDK, lowering the integration barrier for developers already building on OpenAI or anthropic tool chains. The model is priced at $3 per million input tokens and $15 per million output tokens, with cached input tokens dropping to just $0.30. Pricing that positions it roughly in line with mid tier offerings from Western Labs, but at a performance level the company claims approaches the top of the market. A promotional top up rebate running through August 12th offers up to 30% back in vouchers for API credits of $1,000 or more. The benchmark results, drawn from public leaderboard data and a private evaluation by analytics firm Artificial Analysis, tells a striking story. On GDPVAL AA V2, a benchmark measuring real world tasks across 44 occupations and 9 major indust, Kimmy K3 scored 1,687, placing it third overall behind only Claude Fable 5Max at 1815 and GPT 5.6 Solmax at 1747.8, and ahead of Claude Opus 4.8 at 1600. On AA Briefcase, a private agentic benchmark from Artificial Analysis designed to test Long Horizon Knowledge Work, K3 climbed to second place, beating GPT 5.6 Sol Max and trailing only Fable 5 Max. Perhaps most impressively, K3 achieved a state of the art score of 91.2 out of 100 on Browse Comp, a benchmark for long horizon high difficulty information seeking. The company says it accomplished this in a single agent setup using its 1 million token context window without any context compression or additional context management techniques, a feat that suggests raw context length when paired with strong retrieval capabilities may be more powerful than elaborate multi agent workarounds. As one widely followed AI commentator put it on social media, open source is no longer lagging six months behind Western closed source models. Read that again and think about what it all means. That observation captures the significance of the moment. For much of the past three years, open source models have typically trailed their proprietary counterparts by a meaningful margin. Kimi K3 appears to have closed that gap almost entirely beyond raw benchmarks, Moonshot AI showcased a proof of concept that may be even more revealing of K3's capabilities and the company's strategic direction. In a demonstration documented in the company's technical materials, Kimi K3 was tasked with designing a physical chip to run a nanoscale version of itself. Over 48 hours of continuous autonomous agent operation, K3 independently completed the chip's full construction pipeline from architectural design through optimization and verification using open source electric design automation tools. The result was a tiny but functional chip design just 4 square millimeters that achieved timing convergence at 100 MHz and could decode more than 8,700 tokens per second in simulation. This is not a production chip, it is a demonstration of what Moonshot AI clearly views as the next competitive frontier long range autonomous agent capabilities. The ability to sustain coherent multi step technical work over a 48 hour window, reading documentation, making design decisions, running verification loops and iterating on failures represents a qualitative leap beyond the kind of single turn question and answering that defined the first generation of large language models. The company also highlighted a case in computational astrophysics where K3 reportedly reproduced the universal I love Q relation, a complex calculation that typically takes a senior researcher one to two weeks in approximately two hours, reading and cross validating more than 20 papers and implementing a complete numerical pipeline along the way. For enterprise technology leaders, the implications are concrete. A 2.8 trillion parameter open source model that performs at near frontier levels creates new options for companies that want to fine tune, self, host or build proprietary systems on top of a capable base model without being locked into API contracts with OpenAI or Anthropic. The trade off, of course, is that running a model of this size requires substantial GPU infrastructure. Inference at 2.8 trillion parameters is not something that runs on a single server rack. The performance gap between open source and proprietary models has functionally closed at the frontier. If K3's benchmark numbers hold up under independent evaluation, and particularly once the open weights are available for community testing on July 27, it will be difficult for closed source providers to justify premium pricing purely on the basis of capability. The locus of AI innovation, meanwhile, continues to shift. China's AI ecosystem, which many Western observers questioned after early struggles with chip export restrictions, has now produced a model that competes with the best systems from companies with direct access to Nvidia's most advanced hardware. The architectural innovations behind K3, particularly the hybrid linear attention mechanism, suggest that algorithmic efficiency may matter as much as raw compute and the agentic capabilities demonstrated by K3 chip design. Multi week research, compression long horizon information seeking point toward a future where AI models are not just answering questions, but autonomously executing complex multi day projects for enterprises evaluating AI investments. This shifts the value proposition from productivity copilot to autonomous technology technical workforce, end quote. Meanwhile, you know who we haven't heard from in a While? Quoting Bloomberg Alphabet's Google is months behind schedule on delivering Gemini 3.5 Pro, its most powerful flagship AI model, because the company has been taking time to try to improve its capabilities, particularly in coding, according to people familiar with the matter. The delay has been a source of frustration for Google engineers, AI researchers and managers, many of whom are concerned the company risks losing an edge in the market as rivals Anthropic and OpenAI produce models that exceed Gemini's capabilities, according to 10 current and former employees. Google has multiple layers of stakeholders involved in preparing models for release, working to weave AI across a vast product portfolio including search maps and YouTube, which can cause delays, said the people, who declined to be named, discussing internal concerns. Both OpenAI and Meta recently released new models that further outpace Google's current offerings in AI for writing code. Late last month, Google updated the data being used to train Gemini in an attempt to improve these skills, but the results were disappointing, one of the people said. Google's popular products are a gateway to generative AI for everyday people and can yield data that makes their answers smarter. But encouraging leadership of every department to move in the same direction is like trying to boil an ocean, one EX employee said. When mandates shift or efforts end up duplicated in multiple departments, it gets even more difficult to maintain a cohesive strategy, current and former employees said. It's also a challenge for anyone offering to get the resources it would need to succeed and to gain traction in the market, they said. Google co founder Sergey Brin and others were advocating for Google to move faster to seize opportunities in AI coding, but their efforts were slowed by competing factions within the company, two former employees said. Cloud computing unit, Google Cloud research lab, Google DeepMind and the team behind the Android operating system are all building AI coding tools for developers, with involvement from some consumer product teams, too, people familiar with the work said. Efforts to win at coding have also been up against some engineers at Google with a more purist stance who believe that all important code should be human written to adhere to Google standards, EX employees said. Early in the rollout of the technology, employees also faced restrictions on using Gemini to write or analyze software over concerns that proprietary code could leak into the AI model's training data. They said those policies, which have since been relaxed, limited opportunities for engineers to experiment with AI development.
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1-800-contacts. There's so much you need to remember to say during your big meeting, but you're human and sometimes in the heat of the moment, a few points can slip your mind. That's why even G2 exists. Their productivity smart glasses designed with teleprompting, conversation support, real time translation and more. They're also made to look and feel like premium eyewear with no camera and a lightweight 36 gram design you can wear all day. Think of them as your sidekick, helping you stay on script and on point. To learn more about even G2, go to evenrealities.com use promo code techbrew to get 10% off evenring1 and or even clip when you add them to your even G2 order. Major League Baseball has banned the use of league provided dugout iPads to access generative AI for in game strategy calls. Sources say at least a third of teams were already using AI this way, quoting the times. According to a Commissioner's office memo obtained by the athletic, teams were pushing the boundaries of guidelines governing the use of technology by in many cases installing custom apps that expanded the use of the iPads beyond their originally intended purpose to include recommendations regarding substitutions and pitch calling and other in game decisions traditionally made by players and coaches. As much as a third of the league used the dugout iPads for at least one of these purposes, according to people with knowledge of the technology who spoke on the condition of anonymity. The league Memo was issued June 11 and gave teams more than a month to adjust before the ban took effect on Wednesday in time for the start of the season's second half Thursday. The mid season policy change has been met with frustration by some front office members tasked with innovation. It's caused quite the stir, said one high ranking official of an MLB teams research and development department. But even members of more data friendly front offices say the trend raised eyebrows within the industry because of how many decisions made by managers and coaches could be aided by or even potentially replaced by technology such as pitch calling or player substitutions. MLB seemed to harbor similar concerns. The league's review ultimately found all clubs were compliant with the league's rules governing sign stealing and electronic device usage, per MLB's memo. The tech ban therefore did not come with punishment. However, the issue proved urgent enough for the league to act mid season in hopes of curbing the growth of these apps. Said one front office executive. Gotta stop the cheating before there's cheating now. Mlb declined to comment but referred to the memo, which was issued by the league's executive vice president of baseball operations, Morgan Sword. The players union also declined to comment. League provided iPads were first introduced into dugouts league wide in 2016, though their usage was more tightly regulated in 2020 after signed stealing scandals rocked the sport. For the first time, we're going to be allowing players to view in game video via an iPad in the dugout or bullpen during the game, former MLB executive Chris Marinak said in March 2021. The iPads are issued by MLB and fully controlled so that players can only use the app that we've put on the device. They can't access the Internet for browsing or social media or any other kind of functions. The iPads are completely locked down and monitored with the software that we have on there. Entering this season, the league issued iPads provided access to data through three tabs, according to a person with knowledge of the system who spoke on the condition of anonymity. The first contained all the M provided statcast data and multiple video angles. The second tab contained all data related to the automated ball strike system. The third tab, known across the league as the Custom tab, housed each team's specialized data, akin to information found in old school paper filled binders. Examples include matchup info, defensive positioning and player tendencies, which teams argued were necessities going forward. Teams can still upload static information, which is data that would be available before first pitch, though clubs were told that anything uploaded would ultimately remain subject to review by mlb. End quote. Only one long read this weekend, and it's also a bit of a weekend project too. Maybe you should Download the new iOS public beta this weekend to try out the new AI Siri because lots of people are saying it's already changing how they use their iPhone, quoting the Verge before you went to an app and told the app what you wanted to do, call a car, set a timer, order lunch. Now you say what you want to do first, and Siri AI tries to look through all the apps and information available to it to handle the rest. So when I asked about a concert, Siri looked at what was on the webpage, then searched the web, found the answer, and presented it to me. I didn't need to jump around browser tabs or look at the Banning Questions Instagram page. It was just there. In the month I've been using Siri AI, it surprised me in tons of different ways. On my first day testing the beta during Apple's Dev conference, I was able to ask can you add my WWDC briefings to my calendar? And Siri looked in my email, parsed the data, and added six individual events with correct times to my calendar. I'll note it could only add it to my Apple calendar, but I'll get to why shortly. These integrations have genuinely altered my brain chemistry a bit now. I almost always try to use Siri first just to see if it can perform the action I need or answer a simple question. It's practically stopped me from opening my browser for most things, since it's easier, faster, and more enjoyable to just swipe down from the top of the screen and type a prompt on Screen awareness has probably been the most helpful addition for me. Being able to ask Siri about what's on my screen saves me a lot of tapping around. When it's able to take action from that on screen awareness, like adding an event to my calendar or directing me to an address on my screen, it's even better. More often than I've expected, Siri is able to do what I ask it to, and when it performs a somewhat complex task, it feels like magic. But when it hits a wall, I'm reminded of why. It's going to take a bit of work to get it to the it just works universe, where Siri not working is the exception, not the norm. Right now, if you're on the Siri AI Preview on the iOS 27 beta, Apple apps are the only ones with access to Siri's new capabilities. If you live inside Apple's ecosystem, everything is great. Your data probably exists inside messages, mail and photos, and when you want to take action, you're adding to do items to reminders and notes. I use a few of these apps daily, and when they work together, like adding a list of events from my email to my calendar, it really is like a glimpse at the future. But if I ask Siri when did Daniel say he was free to play Dota? Then Siri won't have any idea? Because Daniel and I only message each other through telegram, which the system doesn't have access to. End quote. No weekend bonus episodes for you this weekend. Talk to you on Monday.
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