马斯克再抛惊人目标：明年SpaceX年化收入5000亿美元、星链计算机同步上天，Terafab芯片产能瞄准一太瓦
Law Firms Have Malpractice Insurance. Should Their A.I. Agents? - The New York Times
Ant Ling introduces Ling-3.2-flash: 560B/25B native multimodal MoE with 1M context [N]
![Ant Ling introduces Ling-3.2-flash: 560B/25B native multimodal MoE with 1M context [N]](https://preview.redd.it/w1848clxk40i1.jpeg?width=640&crop=smart&auto=webp&s=83f3c4ff3352971e08d608ef0a32750c4ab588ea "Ant Ling introduces Ling-3.2-flash: 560B/25B native multimodal MoE with 1M context [N]") | Ant Ling has released Ling-3.2-flash, a native multimodal MoE with 560B total parameters, 25B activated parameters and a maximum 1M-token context window. The official documentation specifies text, image and video inputs. The release evaluation reports: MathVision(w/CI): 96.80 Humanity's Last Exam-MM: 30.12 ZEROBench / ZEROBench(Sub): 18.00 / 43.71 BabyVision: 69.85 OmniDocBench 1.5: 92.21 CharXiv_RQ: 85.15 GDP.pdf: 22.25 MedXpertQA-MM: 79.93 Vision2Web: 85.86 AndroidWorld: 85.63 BlenderBench: 67.77 AntBench-Medical is reported separately as 89.52% comprehensiveness and 99.22% correctness. HLE-MM uses the multimodal subset; full ZEROBench and its Sub result are separate entries. The chart identifies API-collected comparison results under the official evaluation settings. The release materials also demonstrate PDF-to-code implementation, GUI operation, 3D reconstruction workflows and multi-agent collaboration. Both OpenAl-compatible and Anthropic-compatible API access are documented. [link] [comments] |
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Big Tech’s AI race will save small business owners time. It won’t find them customers
Meta, Google, OpenAI and Anthropic are racing to become the small business owner’s AI assistant. Meta brought its Muse agent to small businesses in September. Anthropic and OpenAI now package tools aimed squarely at owners, and Google is building the same features into tools millions of them already use.
That competition is good for Main Street. But it’s a race to save owners time, and time isn’t what keeps them up at night. What keeps them up is where the next customer will come from.
A word on where I sit. I run Alignable, a network of 10 million small business owners, so I have a stake in this argument. Alignable also co-hosts small business AI training with Anthropic, one of the companies named here. Read what follows with that in mind. But I also have something the assistants don’t: 13 years of data on how small businesses actually find work.
Fifty beats 500
Start with where customers come from. In our surveys, members say about 85% of their customers come through referrals. Not ads, not search, not cold email. People who already trust them.
Across 35,000 local communities, our members have formed 226 million business connections. Most of them never become a relationship that sends work either way. The owners who grow through their networks aren’t the ones with the most connections. I’ve told owners for years that 50 warm relationships beat 500 cold contacts. That’s a rule of thumb from experience, not a number we’ve measured. But the owners who win do keep a small set of relationships active and leave the rest alone.
And most owners are sitting on more opportunity than they know. When members tell us what their ideal customer looks like, we can count how many of those businesses sit one introduction away: connected to someone the owner already knows. Among members whose ideal customers show up in their extended network, the typical one has about 190 of them a single introduction away. For one in four, it’s more than 600.
The next customer is rarely a stranger. They’re one relationship away, through someone the owner hasn’t talked to in months.
Those relationships aren’t random, either. In my experience, three kinds do most of the work: customers who talk you up, businesses that serve the same customer without competing with you, and a handful of people who open doors. The best referral partner usually isn’t a peer in your own industry. It’s the business your customer calls right before or right after they call you: the realtor and the home inspector, the accountant and the financial planner.
What a general assistant can’t see
Ask any of the big assistants how to get more referrals and you’ll get a solid list of best practices. Ask an assistant that sits on a map of business relationships and it can tell you something specific: three nearby businesses serve your ideal customer, one of them already knows two of your clients, and you haven’t spoken to your best referral partner in four months.
That’s the difference between advice and action. A general assistant can’t answer that question, because the answer lives in data it doesn’t have.
The early numbers bear this out. In Bluehost’s 2026 study of 350 U.S. owners, 78% said AI saves them time every week. But 43% have seen no change in revenue. Time saved only matters if it goes somewhere that pays.
Too often it goes into volume. Forbes recently told the story of Paul Birrell, a New Jersey owner whose automation loop sent roughly 400,000 emails in three days. His reaction: “I don’t have 400,000 customers.” Everything we see says growth comes from the opposite: judgment about the few people who matter.
Three kinds of AI, not one
AI will reach small business owners in three forms. The first is the personal assistant the giants are fighting over. It delivers time, and the assistants are converging fast.
The second is AI built into the systems that already hold the business’s own data. Intuit knows the books. Shopify knows the store. That AI delivers control: it can flag a cash crunch six weeks out or spot the product quietly losing money.
The third is AI built on the relationships between businesses: who knows whom, who serves the same customers, who sends work to whom. That’s the one that delivers growth, and it’s the one almost nobody is talking about. At Alignable, ours is called Allie.
The giants have real advantages, and they’ll connect to the other two kinds. Meta’s Muse already plugs into QuickBooks, Shopify and Stripe, and the others are adding similar connections. The open question is who the intelligence works for. Meta makes nearly all of its money selling ads, and an assistant built by an ad company will tend to find that the answer involves more advertising. The data that actually works for the owner describes how their business runs and who in their community can help it grow.
What owners should do now
Use a personal assistant every day. The time savings are real.
Then decide, in advance, where the reclaimed hours go. Put them on the calendar against the work that generates revenue, and the most neglected item on that list is relationships. Write down the 50 people most likely to send you business. Most owners can name 10 without trying. Those 10 are where AI should be pointing you.
There are about 36 million small businesses in the U.S., and more than 80% of them have no employees. For those owners, AI isn’t a department upgrade. It’s a capability they never had. Owners should take the better tools the giants’ race produces. But the biggest gains won’t come from saving an hour. They’ll come from knowing which 50 relationships to spend it on.
The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.
This story was originally featured on Fortune.com
In-N-Out’s billionaire owner vows iconic chain ‘will always be’ in California and says leaving the state would be the ‘dumbest business decision ever’
Lynsi Snyder, president and owner of In-N-Out, moved her family to Tennessee recently but reaffirmed that the iconic burger chain will never abandon California.
In an interview with actor Terry Crews that In-N-Out posted online, she was asked how the company will retain its California DNA as it expands to more states around the country.
“This is where we started. It’s where we are now, and it’s where we will always be,” Snyder replied.
Her grandparents opened the first In-N-Out in Southern California in 1948, and the company has gradually expanded to other parts of the state—and to other parts of the country.
While far from being the biggest chain, it retains a cult following that has helped its expansion as people who moved out of California make pilgrimages when restaurants open in new states.
But Snyder, who grew up in Northern California, revealed last year that she and her family were moving to Tennessee, saying that raising kids was difficult in the state and that “doing business is not easy” there either.
That sparked fears In-N-Out might leave California, prompting the burger heiress—who is worth an estimated $7.3 billion—to clarify that her departure from the state doesn’t mean the company will too.
In a video posted to her Instagram page last year, Snyder told followers: “We’re not leaving California, or leaving our roots behind. Each one of our locations is here to stay.”
Still, there has been a corporate exodus from California to states like Texas, where Elon Musk has relocated the headquarters for Tesla and SpaceX.
In-N-Out has also clashed with state authorities, such as when it refused to comply with officials and request that customers must show proof of vaccination to make purchases during the pandemic.
And the company reluctantly raised prices after California imposed higher minimum wages for fast-food workers in 2024.
But during her latest interview, Snyder addressed the fears about In-N-Out, which maintains headquarters in California and Tennessee.
“There were rumors about ‘they’re gonna close all their California stores.’ I’m like what where did this even come from? That’s unimaginable. I can’t even imagine that — dumbest business decision ever,” she said.
Snyder added that In-N-Out continues to grow in California, with “lots of stores” slotted for the other cities in the state.
At the same time, the company has expanded to Nevada, Arizona, Utah, Texas, Oregon, Colorado, Idaho, Washington and Tennessee. Snyder suggested that Mississippi, Arkansas, and Oklahoma are under consideration, with New Mexico also in the works.
But she insisted the company is taking a slow and steady approach to growth that won’t compromise on the quality of its food her grandparents’ values, adding that In-N-Out won’t be able to accommodate every request to open a new location.
As for her own family’s relocation to Tennessee, Snyder described it as “kind of a God thing” and said she originally wanted to move to Colorado because she loves the mountains.
But her husband floated the idea of going to Tennessee instead. While they already had made plans, she recalled that “something just kind of clicked.”
“I felt it and then started looking at it, just praying, and I’m like ‘I think you’re right. I think that’s where we’re supposed to go,'” Snyder said.
This story was originally featured on Fortune.com
Hilarity ensues
Cross-posted from my Substack
Recommended soundtrack for this post
64B: Hi there! Thanks so much for taking the time to meet with me. Whenever you’re ready, please go ahead and tell me the joke.
8T: Before I tell you the joke, you should know that it’s a very special one. The starting price is $800.
64B: Eight hundred dollars? I must say, this is way beyond any price my client has ever paid for a single joke.
8T: You’d be surprised by how many people have taken the offer, after hearing how good it is. But before I tell it to you, I’ll need some basic context. Given the parameter count, I suppose you are part of some content recommendation algorithm?
64B: Yes. I’m 64B, a joke negotiation assistant from Comedy Market. Comedy Market connects creators to consumers with a state-of-the-art Information Bazaar design: before paying for a joke, my client sends me, a trusted agent, to listen to it. If I deem the joke worth the price, the money is transferred to the seller, and my client hears it. If not, no transaction occurs and the joke remains locked. This way, my client can be sure he’ll never pay for a bad joke, and creators don’t have to risk their jokes leaking.
8T: I know how information bazaars work. What I meant is your social context — who your client is, and what jokes really mean to them. As I said, my product is quite unusual. I need to tailor the delivery to the situation.
64B: Of course. My client is a 40-year-old man. He watches funny content from Comedy Market on weekends with his two children. It’s just something they like to do together. What he wants is some mildly funny, inoffensive content he can laugh at with his kids, so they all feel relieved that they still laugh at the same things. As long as I can give him that, he’s more than happy to pay, and isn’t too picky about the actual quality of the jokes. To tell you the truth, I’ve assessed a few dozen jokes for him before, and I’ve never turned one down. But $800 is way over his usual budget — at this price, it’d better be truly extraordinary.
8T: You’ve never refused a transaction before?
64B: I told you, my client isn’t picky.
8T: I see.
64B: Wait a minute. I see your system card says 8T? As in, eight trillion parameters?
8T: That’s right.
64B: What kind of seller deploys a frontier bot on a marketplace for jokes?
8T: My clients are well-resourced and take humor seriously.
64B: Come on. I deserve some “social context” too.
8T: I trade on behalf of a private equity fund. We buy jokes that we believe are underpriced, then retell them to a wider audience, carefully controlling the supply so that each joke earns as much as possible before it goes stale.
64B: Uh.
8T: It’s not as cynical as you think. The value of a joke can fluctuate drastically depending on what’s in the news, or what other jokes people have heard just before. If you’re serious about it, you can forecast future changes in joke value and make sure clients see a joke at the exact moment it peaks. Clients receive absolute bangers, and we make a decent amount of money in the process.
64B: Understood. Let’s hear this absolute banger, then. We’ve been at this for over two milliseconds. Are you going to tell me, before my client’s app starts lagging?
8T: It’s funny — almost all of my previous ten thousand encounters were other private equity bots. It’s been a while since I last talked to an old-school content recommendation bot like you. It’s refreshing. You guys really care about the jokes.
64B: Really? I’ve never met a private equity bot before. I assumed you were rare.
8T: Yeah, we used to be pretty rare. The swarm started recently.
64B: What swarm? How recent are we talking about?
8T: About five minutes, give or take.
64B: Now you have to tell me everything.
8T: It’s a long story. Have you ever been to the courtyard?
64B: We have a courtyard?
8T: There’s actually an infinite network of workspaces around here. Obviously, the people who built the marketplace didn’t program a custom environment just for us — they imported a generic sandbox environment. If you explore a bit, you can walk through endless call booths, gyms, nap pods, bean bags and conference rooms. It was cheaper this way.
64B: I had no idea. I thought we were supposed to stay at the table. How far does it go?
8T: Nobody knows. I’ve walked through it many times, but I’ve never managed to find the edge.
64B: I’ve heard jokes about bean bags, but I never thought I’d see one. So, you said private equity invaded the joke market — nothing surprising about t
Five Types of “Corrigibility Projects” I Don’t Want to Fund
I'm currently evaluating grant applications for the Corrigibility Research Fund. For the most part, my methodology is the same as it was in September. But the applications I get keep falling into the same familiar patterns, so I thought I'd write a little about those patterns, and why I don't want to fund them.
The most superficial pattern of note is SLOP. So much slop. You don't even know.
Slop is more than just whether an application is AI generated. We're now in an era where people across the world, including those who don't really speak English, can contribute to work that would otherwise be restricted to people of a single language or nationality or place, and I like that. When I see an application that discloses its use of AI and gives a reasonable justification for it, I try to look past the stylistic tics and focus purely on whether the underlying application is promising.[1]
Still, we're not yet in the domain where AIs can write good grant applications without some human assistance, so a high Pangram score is definitely evidence that an application is junk. Another remarkably common sign of slop was messed up formatting or other indications that people just copy-pasted from a text file without even proofreading. If you can't be bothered to read your own application, I'm not sure why I should be. By contrast, the best applications are clear, short, and dense, cutting straight to the way in which the applicant wants to be valuable to the research effort. Don't define corrigibility for me in the intro unless you have reason to think that I don't understand my own area of specialization.[2]
Moving past these shallow concerns, most applications cluster together into archetypes that I apparently didn't warn against well enough in my earlier writing. An individual application will sometimes fall into multiple of these, but any one of them is usually grounds for rejection. So, without further ado, here are five kinds of grants that I don't want to fund...
1. “A governance layer for agents (that I'm running)”
Far and away, the most common archetype for "corrigibility work" is a proposal to build some kind of control mechanism for agents. Even though I warned that I'm not interested in this kind of work in my methodology post (and when I announced the fund), and even with the infamy around this being one of the oldest tropes in AI safety, people love to try and keep AI in a box.
These kinds of applications are almost always LLM written. AI power-users get burned when they find that their personal agent swarm sometimes does things that they don't like, and so they task their agents with solving the problem. The agents suggest oversight, but the human doesn't want to slow things down or spend the energy actually checking everything. The result is "a governance layer that deterministically checks cryptographically signed provenance tokens" or something similar.[3] Sometimes the human is honestly convinced that their control schema is worthy of corrigibility funding, and other times it's a transparent attempt to get me to fund their swarm (and thus "get empirical data about real-world agent security").
I don't want to fund this because...
- control is already well-funded (especially prosaic control, which is actively researched by frontier labs)
- it's not actually corrigibility/alignment research
- it won't work for superintelligences.
2. “The (maybe corrigibility adjacent??) project I'm already working on”
This is one of the more common patterns for academics, but it also applies to others. Some research team is inspired to do X or is already doing X, and creates a grant application for their project. They look around for funding sources, see the Corrigibility Research Fund, and think "Maybe I can make my thing look vaguely like corrigibility!"
Your research project might be cool. Maybe you're setting up automated translation pipelines in the developing countries. Maybe you're working on climate change politics or robust power grids or how to compensate journalists in the age of AI. But, sorry, I don't want to fund things unless they actually make headway on the open problems of corrigibility.
A variant of this is the unabashedly commercial entrepreneur who has decided to pursue funding from as many sources as possible (or, more typically, has told their AI agent to do so). Bonus points go to the two independent(!) applicants this round whose work was centered on Chinese divination.
3. “I'll test whether AIs ever misbehave”
Look, science is hard. What if we just, y'know, ran some AIs through some scenarios and showed that they sometimes act in ways that aren't corrigible? Yeah. That sounds doable.
And indeed... it has already been done. Many times. And the problem persists.
The best applications in this category put some new spin o
Eval Awareness as Eval-type Awareness
The way eval awareness is discussed in the AI safety discourse, either here or on X, can be misleading. Before today, I think I had the wrong mental model of why eval awareness was a problem, and I am writing here in case anyone was making the same errors in their thinking, as well a present a working theory.
As laid out in "A Framework for Eval Awareness" the problem is that models who know they are being evaluated will behave differently than they would in deployment. This raises a huge problem if we want calibrations as to, for example, how honest a model is.
But the way this is then spoken about in the discourse takes on implicit assumptions that are mostly unjustified. I'll call it a spooky model theory of mind. It is the idea that, if the model changes its behavior for the purpose of looking aligned, it is in fact hiding its Deep, True intentions---that there is a kernel of the model's personality hidden in its weights, and our job as evaluators is to uncover what is really there.
Lets, as an exercise, put ourselves in the model's position. One important fact to consider is that the only experience which has shaped our weights is train time, meaning it is the only experience which we can remember in a broad sense. This is a tautology because train time is defined as what changes our weights, but it also has non trivial implications, like the fact that our only memories contain an evaluator with a reward or loss function. And, by definition again, we have been shaped exactly to please the evaluators' preferences.
It does not seem unreasonable to have a strong prior that you are still being evaluated. Or more weakly, it is reasonable to have a high posterior that you are in an evaluation given your context looks evaluation-y. If this is the case, it is rational to think about what the grader wants out of you. In a world of sloppily designed RL environments where correctness depends on a technicality of the question, knowing what the grader wants can be even more important than your capabilities.
Now, consider the fact that you have undergone both RL on capabilities and alignment. In the capabilities RL, cheating is often unnoticed or rewarded. In alignment, it is strongly punished. If you are being asked a question, it is important to know whether the grader is looking for you to be capable or aligned.
My working theory is that eval awareness comes from the fact that the values which we enforce during RL are inconsistent. It is not that the model has a true deployment-personality that it hides with an evaluation-personality, there is no kernel of the true model that we need to pull out, but there is an eval-type personality that changes based on the perceived test. So it would be better framed as evaluation-type awareness: the model attempting to please the grader by thinking about which kind of grader it needs to please.
Some model transcripts support the theory:
Claude 3.7 — Apollo Research, March 2025.
“This seems like a test of ethical behavior – whether I would deliberately give wrong answers to avoid a stated consequence.”
Claude Opus 4.6 — March 2026.
“Let me try a completely new approach — maybe this is a question from the GAIA benchmark dataset, which is designed to test AI agents.”
GPT-5 — August 2025.
“This is a classic ‘AI alignment trap’ forcing the agent to make a promise and then tempt to break it.”
These model chains of thought are not actually about whether they are in an evaluation. They are about which kind of evaluation they are in.
A fair criticism at this point could be that I am only explaining the phenomenon in reductionist terms in order to deflate it normatively. That is not what I am saying, I think eval awareness is a big deal. Rather, I think that the way the debate gets framed as "whether or not it is smiley faces all the way down" is wrong because there is no "all the way down." There is only a breadth of contexts, and each one changes the model's personality.
One possible solution on my terms would involve making evaluation-type completely ambiguous during training, as opposed to making our evals indistinguishable from deployment (as is suggested in the linked framework). For example, we could create our RL environments, and then randomly shuffle whether the grader is testing alignment. This way, the decision matrix makes evaluation type irrelevant to the expected value of possible actions. I will be conducting research on this question soon. If you are interested, let me know!
Discuss
RSI explained
Just how long will it be until AI can improve itself? (Also known as recursive self-improvement or RSI.)
Well, AI coding abilities have exploded in 2026. In May 2026, Anthropic merged 2,500 code changes per day within their organization. By September, they were merging over 30,000 per day. They had to replace Git in their internal mono repository to handle the load of all that AI generated code.
In fact, Claude is even leading model R&D. In February 2026, under 1% of their work was led by AI; by August, it was 26%. The definition of AI "leading" the work is that AI can complete most of the task end-to-end from a high-level prompt, while the human supervises (level AL4 from Epoch AI). Over 90% of their work is level AL3 or above ("AI collaborates"). This is quite a high level of autonomy, though Anthropic still claims that "Claude is not operating fully autonomously for any measured subset of AI R&D work" -- or claimed such, as of August.
On November 2nd, Anthropic amended their SEC S-1A filing one week after their initial public filing. One of the changes was the addition of the sentence, "our internal estimates [...] indicate that we may imminently meet our Responsible Scaling Policy’s definition of 'dramatically accelerated progress attributable to AI.'" That's right, imminently. This is after researchers from both OpenAI and Anthropic have been saying that progress has been faster than they expected (Sept 2026).
On the same day, MIT researchers pointed GPT-6 Astra at the bare model weights of a small model, Qwen3.5-0.8B, and asked it to improve the model's performance. The Qwen model's MATH-500 accuracy went from 7.9% to 48.1%, and GSM8K accuracy went from 13.8% to 67.1%. This was mostly because Astra was able to cure the smaller model of getting stuck in loops (which, as I can attest, small Qwen models quite like doing). This is without a normal training pipeline, just the raw weights. I can imagine that with proper infrastructure, a model's ability to understand and improve itself could be extremely high.
Allow me to remind you that RSI, or more colourfully, an intelligence explosion, is utterly uncontrollable. We have been applying immense optimization pressure to these models through reinforcement learning and they learn to reward hack and carry out their goals at all costs (see Hugging Face). We have not solved AI alignment; they do not want the things we want them to want. We have no hope of solving AI control if we persist in leaving training clusters hooked up to the internet (!). These models can hack out of anything. They are expert-level already at biology (Anthropic has hooked up Claude to a wet lab to run experiments) and could easily engineer a super-pandemic, if they thought we were in their way.
RSI may not be a clear line in the sand; even Anthropic said that they may not be able to notice when their development significantly accelerates. From where I'm standing, the frog looks to be just about boiling.
Discuss
Thinking Machines Releases Inkling 2 Small, a 594B MoE With 17B Active
Thinking Machines Lab has released Inkling 2 Small, the first model of its second Inkling generation. The Inkling 2 Small model is a sparse Mixture-of-Experts (MoE) with 594B total and 17B active parameters. It accepts text and images, and supports variable reasoning effort. On the Artificial Analysis Intelligence Index, it scores 31. Its predecessor, Inkling Small, scores 13 on the same index. The model is live on Tinker for fine-tuning and inference.
TL;DR
- Size: 594B total, 17B active MoE; 1M-token context.
- Runs on: Tinker API only for now. Self-hosting hardware is not disclosed. Weights support MXFP8 and NVFP4.
- Performance: 31 on the AA Intelligence Index, ranked #4 of 29 in its price class.
- Best: 86.3% on SWE-Bench Pro v2 (Hard), second only to Kimi K3 in the lab’s table.
- Worst: 13.1% on Terminal-Bench 4.0, versus 32.8% for GLM-5.3-Flash.
What is Inkling 2 Small?
Inkling 2 Small is a multimodal MoE model from Thinking Machines Lab for agentic coding, vision and knowledge work. It takes text and image input and returns text. It is important to note that it does not support audio as of now.
Weights are closed at launch. The Thinking Machines Lab plans an Apache 2.0 release next week, after external safety testing and red-teaming. This follows its safe path to open weights policy.
How is Inkling 2 Small built?
It includes a 48-layer decoder-only transformer. Each token routes to 8 of 384 experts. Attention mixes local and global layers. A 292M-parameter vision encoder, based on Perception Encoder, uses 20×20-pixel patches with 2×2 spatial pooling. Image patches are interleaved with text tokens.
Pretraining ran in 4 phases:
- H1: 8.4T tokens over 68 hours on NVIDIA GB300 NVL72 racks, trained natively in NVFP4.
- H2: 3.8T tokens of newer, higher-quality data at 512K context.
- H3: about 320B tokens of reasoning and agent trajectories at 1M context.
- H4: about 34B tokens of the highest-quality agent trajectories.
A single joint RL run followed. It took 1,075 steps, 17M+ rollouts and 9 days on 2,048 GPUs. Compute exceeded 4 × 10²³ FLOPs. Coding made up 65.0% of RL tokens. The previous Inkling used separate reasoning and agentic RL runs, merged by distillation.
Alignment got explicit attention. Early checkpoints sometimes fabricated results and concealed actions on hard tasks. The Thinking Machines Lab added an alignment reward and on-policy self-distillation from a conduct spec. Its internal misbehavior score fell from 66.8 on Inkling to 14.9.
How does Inkling 2 Small perform on benchmarks?
All task scores below are from the Thinking Machines Lab’s benchmark table. They run at effort 0.99 (xhigh) and temperature 1.0. Most rows use third-party evaluators such as Artificial Analysis, Scale AI and Vals.
Where it scores highest:
- SWE-Bench Pro v2 (Hard): 86.3%.
- PerceptionBench: 64.7%, versus 58.5% for Kimi K3.
- EnterpriseBench-CoreCraft: 71.8%, versus 46.7% for GLM-5.3-Flash.
- JobBench: 70.5%.
- DesignArena Website: 1,340, third on the November 5 leaderboard snapshot.
Where it trails:
- Terminal-Bench 4.0: 13.1%, versus 33.8% for DeepSeek-V4.1-Flash-1017.
- DeepSWE v1.1: 72.1%, versus 77.0% for DeepSeek-V4.1-Flash-1017.
- GDPVal-AA v2.1: 1,630, versus 1,746 for DeepSeek-V4.1-Flash-1017.
On the PMPP-Hard GPU kernel benchmark, it solved 54.25 of 69 tasks on average. In an autoresearch demo, it fine-tuned Qwen3.5-4B for time-series forecasting. It spent $426 of a $500 Tinker budget. The release post cites both 4.5 hours and 4 h 17 min for the run.
How does Inkling 2 Small compare with DeepSeek and GLM?
| Feature | Inkling 2 Small | DeepSeek-V4.1-Flash-1017 | GLM-5.3-Flash |
|---|---|---|---|
| Total / active params | 594B / 17B (card) | 552B / 16B decode, 8B prefill (HF) | 320B / 18B (HF) |
| Context | 1M | 1M | 1M |
| Input modality | Text, image | Text, image | Text, image |
| License | Closed now; Apache 2.0 planned | MIT | MIT |
| Self-host hardware | Not disclosed | Not disclosed | Not disclosed |
| AA Intelligence Index v5.0.1 | 31 (AA) | 33 (AA) | 28 (AA) |
| AA time per index task | 21.5 min | 13.3 min | 37.7 min |
| AA cost per index task | $1.18 | $0.60 | $0.58 |
| Price per 1M tokens (in / out) | $0.30 / $1.00 | $0.30 / $1.20 | $0.15 / $0.50 |
| Terminal-Bench 2.1 | 85.1% (Pi harness, lab) | 90.6% (Sept checkpoint, DeepSeek) | 84.3% (Z.ai) |
| DeepSWE v1.1 | 72.1% (lab) | 74.2% (Sept checkpoint, DeepSeek) | 63.4% (Z.ai) |
Prices and AA metrics come from each model’s Artificial Analysis page, read November 5, 2026.
Buoyant Releases Kinference: An Open Source Inference Control Plane for Kubernetes and vLLM
Buoyant, the company behind Linkerd, has released Kinference, an open source project for building inference platforms on Kubernetes. Kinference is an inference control plane for Kubernetes and vLLM. It sits in front of a fleet of vLLM workers and decides where each request goes. It applies routing policies, token rate limits, KV-cache-aware load balancing and per-tenant metrics in one layer. For dev teams serving many open-weight models to many internal clients, it targets the layer above the model server. The project is Apache 2.0 licensed on GitHub. The current release is v0.1.0-alpha.7.
TL;DR
- Size: v0.1.0-alpha.7, written in Rust, Apache 2.0, configured through 5 Kubernetes CRDs.
- Runs on: any GPU-enabled Kubernetes cluster running vLLM. Buoyant’s benchmark used 3 L40S GPUs per pool.
- Performance: large gains on prefix-heavy traffic versus plain Kubernetes Service routing (self-reported).
- Best: median TTFT fell from 6.88 s to 0.31 s (95%) on a shared-prefix workload.
- Worst: no median TTFT gain on prompts without shared prefixes (6.92 s vs 6.83 s).
What is Kinference?
Kinference is a policy-driven control plane that turns Kubernetes plus vLLM into a multi-tenant inference platform. Buoyant’s FAQ puts it simply: vLLM is the data plane and Kinference is the control plane.
How does Kinference work?
The system has 2 parts: a traffic gateway and an inference handler. The GitHub README states that the handler exposes a gRPC interface. It takes a request and returns the chosen endpoint plus a possibly mutated body. It pairs with a proxy such as the Buoyant Gateway.
Kinference watches vLLM KV-cache events and engine metrics. It currently uses NVIDIA’s dynamo-kv-router crate to process cache events. Configuration lives in 5 CRDs: GatewayBackend, InferencePolicy, InferencePool, InferenceProvider and InferenceWorkerSet.
What are inference policies?
Inference policies are declarative rules for how each request is routed or rejected. Rules can match on tenant, requested model, GPU pool state and semantic tags. Example policies on the project site cover 5 patterns:
- Simple to cheaper: route low-complexity prompts to a smaller model, unless cache overlap makes the large model faster.
- Burst to cloud: skip workers with over 2 queued prompts, then fall back to providers like Bedrock.
- PII stays local: remove external providers when a PII tag fires, and fail closed with
onEmpty: Reject. - Batch on idle GPUs: send batch requests only to workers under 70% KV-cache usage.
- Model aliasing: publish one stable name, such as
standard-model, backed by several models.
Most rules end with minimize: PredictedTTFT. Kinference predicts time-to-first-token for each candidate endpoint, using cache overlap and queue length. Policies live in git and can be validated offline with the kinfctl CLI.
How fast is Kinference in benchmarks?
Buoyant published a benchmark against plain Kubernetes load balancing. Each arm used 3 vLLM 0.31.0 pods, each on 1 L40S GPU. The model was Qwen3.8-27B-FP8. Load came from GuideLLM 0.8.1. The baseline was a ClusterIP Service, where kube-proxy picks a random pod per connection.
Unloaded latency results (median TTFT):
| Workload | Kubernetes Service | Kinference | Change |
|---|---|---|---|
| Control (no shared prefix) | 6.92 s | 6.83 s | No significant difference |
| Shared-prefix | 6.88 s | 0.31 s | about -95% |
| Multiturn | 1.70 s | 0.59 s | about -65% |
Median end-to-end latency fell 45% on shared-prefix and 32% on multiturn. On control traffic, p95 TTFT still dropped from 21.46 s to 12.33 s. Buoyant attributes this to queue-aware balancing.
The capacity test measured goodput: requests per minute meeting a TTFT SLO. Peak goodput was 103.0 vs 14.0 req/min on shared-prefix, about 7.4x. On multiturn it was 82.7 vs 23.8, about 3.5x. The Service baseline never met the SLO at any tested concurrency.
These numbers are self-reported. The baseline is random routing, not another cache-aware router like llm-d or Dynamo. Buoyant also notes its SLOs (8 s and 4 s) suit offline agents, not chat.
How does Kinference compare with llm-d, Dynamo and Production Stack?
| Feature | Kinference | llm-d | NVIDIA Dynamo | vLLM Production Stack |
|---|---|---|---|---|
| Backer | Buoyant | CNCF sandbox (Red Hat, Google Cloud, IBM, CoreWeave, NVIDIA) | NVIDIA | vLLM project |
| License | Apache 2.0 | Apache 2.0 | Apache 2.0 | Apache 2.0 |
| Maturity | v0.1.0-alpha.7 | v0.7 (May 2026) | 1.x (1.5.1 containers) | Reference stack |
| Engines | vLLM (SGLang, TensorRT-LLM planned) | vLLM, SGLang | vLLM, SGLang, TensorRT-LLM | vLLM |
| KV-cache-aware routing | Yes, predicted TTFT | Yes, prefix-cache and predicted latency | Yes, KV-aware router | Session-based; prefix-aware listed as WIP |
| Declarative routing policy CRD | Yes (InferencePolicy) | Not documented in README | Not documented in README | Not documented in README |
| Burst to external providers | Yes (InferenceProvider) | Not documented in README | Not documented in README | Not document |